# templates

name: Automate Interview Scheduling and Data Cleanup with Cal.com and Google Sheets
author_name: Abdulaziz
author_username: abdulaziz747
categories: []
created_at: 2025-06-30T13:27:37.443Z
updated_at: 2025-06-30T13:27:37.443Z
url: https://n8n.io/workflows/5481
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  Workflow Description*
  Automate your candidate interview pipeline with precision.
  This powerful integration pulls booking data from Cal.com, extracts interview details (name, email, date & time), and syncs them directly into your Google Sheets tracker. It matches applicants using email, formats the date in the Asia/Riyadh timezone, and appends only relevant entries.

  But it doesn’t stop there — it cleans your sheet automatically by removing unmatched or empty records, ensuring your data stays clean, reliable, and ready for HR decisions.

   What it does:
   Fetches interview bookings from Cal.com

   Converts time to readable format (e.g. 30 June, 09:00 AM)

   Matches booking emails with existing applicant records

   Auto-updates interview date in your sheet

   Detects and deletes irrelevant or blank entries

   Use Cases:
  Resume screening workflows that require automated interview sync

  HR dashboards needing real-time calendar updates

  Applicant pipelines where only valid interviews should remain

  Ideal for:
  Recruiters • HR teams • Automation architects • Remote hiring pipelines

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.googleSheets","n8n-nodes-base.splitOut","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.splitInBatches","n8n-nodes-base.if","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.httpRequest"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","data processing","hr"],"complexity":"medium","use_cases":["Automatically sync Cal.com interview bookings to a Google Sheet","Keep HR dashboards up-to-date with interview times","Match bookings to applicant records by email","Automatically remove empty or unmatched sheet rows to keep data clean"],"estimated_setup_minutes":25,"required_services":["Cal.com (API/webhooks)","Google Sheets (Google account/Drive)"],"key_features":["Fetches bookings from Cal.com","Parses and formats interview datetime to Asia/Riyadh","Matches booking emails to existing applicant rows","Updates or appends interview dates in Google Sheets","Detects and deletes irrelevant or blank records to clean the sheet automatically"],"target_audience":["Recruiters","HR teams","Automation architects/engineers who build hiring pipelines','Hiring coordinators"]}


name: Auto-Generate & Post AI Images to Facebook using Gemini & Pollinations AI
author_name: Fahmi Oktafian
author_username: fahmi3322
categories: []
created_at: 2025-06-30T17:33:43.481Z
updated_at: 2025-06-30T17:33:43.481Z
url: https://n8n.io/workflows/5486
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:33
description:
  This workflow is designed for content creators or AI artists who want to automate posting unique AI-generated images to their Facebook Page multiple times a day. It uses Google Gemini via LangChain to generate imaginative image prompts, and Pollinations AI to generate the images. Posts are published with hashtags and a clean caption.

  Who Is It For
     AI artists
     Facebook page managers
     Digital marketers looking for automated creative content

  What It Does
     Triggers 3x daily at 7:00, 11:00, and 17:00 (local time)
     Generates random AI image prompts in a retro-futuristic, cinematic, or surreal style using Google Gemini
     Fetches images from Pollinations AI using custom prompts
     Posts images automatically to your Facebook Page with hashtags

  Requirements
     n8n self-hosted or desktop (workflow uses schedule trigger)
     Pollinations API (no auth needed)
     Facebook Page with Facebook Graph API token:
     Required scopes: pages_manage_posts, pages_read_engagement, pages_show_list
     Google Gemini API Key (used via LangChain node)

  How to Customize
     Change the prompt style in the Basic LLM Chain node (promptType: define) to suit your theme.
     Adjust Set The Generator Image node if you want:
     Different image sizes (width / height)
     Different seed randomness
     Other Pollinations models (&model=kontext)
     Add Telegram/Twitter nodes if you want cross-posting
     Use Set node to allow easy user-level configuration of models, hashtags, times, etc.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.scheduleTrigger","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.facebookGraphApi"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","social media","content creation","AI image generation","integration"],"complexity":"medium","use_cases":["Automatically post AI-generated images to a Facebook Page","Generate creative image prompts with Google Gemini","Create scheduled content for engagement and consistency","Automate creative workflows for AI artists and marketers"],"estimated_setup_minutes":30,"required_services":["n8n (self-hosted or desktop)","Google Gemini (API Key via LangChain node)","Pollinations API","Facebook Graph API (Page token with pages_manage_posts, pages_read_engagement, pages_show_list scopes)"],"key_features":["Schedule trigger (runs 3x daily at configurable times)","Prompt generation using Google Gemini via LangChain","Image generation via Pollinations AI with configurable size, seed, and model","Automatic posting to Facebook Page with clean captions and hashtags","Easily customizable prompt styles and model/settings via Set nodes"],"target_audience":["AI artists","Facebook page managers","Digital marketers"]}


name: Create Debug Breakpoints and Logs with Slack Interactive Messages
author_name: Wyeth
author_username: wyeth
categories: []
created_at: 2025-06-30T18:37:54.974Z
updated_at: 2025-06-30T18:37:54.974Z
url: https://n8n.io/workflows/5490
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:34
description:
  How it Works:
  You can now use the Slack node to create conditional breakpoints! This example shows the loop stop on 4 (of 10) and then you can hit "continue" in Slack when you are ready. 

  You could extend this with a Form node and inject values you type in on the fly.

  Slack was already the best place to debug log n8n, and now it just got better!

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.slack","n8n-nodes-base.noOp","n8n-nodes-base.noOp","n8n-nodes-base.splitInBatches","n8n-nodes-base.debugHelper","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.if"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","debugging","collaboration"],"complexity":"medium","use_cases":["Interactive debugging of n8n workflows","Creating conditional breakpoints in loops","Remote pause/resume of workflow execution via Slack","Sending debug logs and prompts to a team channel","Injecting runtime values (via Form node) to influence execution"],"estimated_setup_minutes":15,"required_services":["Slack (workspace & bot/app credentials)","n8n instance (cloud or self-hosted)"],"key_features":["Use Slack interactive messages to create conditional breakpoints","Pause looped executions (e.g., stop on item 4 of 10) and resume from Slack","Integrates splitInBatches + if nodes to control flow","Emit debug logs and context to Slack for easy inspection","Expandable with a Form node to inject runtime values from Slack"],"target_audience":["Developers","DevOps / SRE","Automation engineers / Integrators"]}


name: Generate & Send Spare Parts Price Quotes with Gmail, Sheets and Gemini AI
author_name: berke
author_username: berke
categories: []
created_at: 2025-06-30T19:47:37.078Z
updated_at: 2025-06-30T19:47:37.078Z
url: https://n8n.io/workflows/5492
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  Who's it for

  This workflow is perfect for sales teams, customer service departments, and businesses that frequently handle spare parts inquiries via email. It's especially valuable for companies managing multiple products with complex pricing structures who want to automate their quotation process while maintaining professional, multilingual communication.

  What it does

  This workflow:

  Monitors your Gmail inbox** for incoming spare parts requests  
  Automatically generates professional HTML price quotes** in the sender's language  
  Sends personalized replies**  
  Uses AI to detect the email language (supports Turkish, English, German, and more)  
  Extracts project or part codes**  
  Fetches pricing data from Google Sheets**  
  Calculates totals accurately**  
  Formats everything** into a clean, professional quote that matches your brand  

  How it works

  Schedule Trigger runs every minutes to check for new emails  
  Gmail node fetches the latest unread email  
  Keyword detection filters for spare parts-related terms in multiple languages  
  AI Agent processes the request by:  
     Detecting the email's language  
     Extracting project/part codes  
     Querying three Google Sheets: CRM, Bill of Materials, Pricing  
     Calculating line totals and grand total  
     Generating a professional HTML quote in the sender's language  
  Gmail reply sends the quote and marks the original email as read  

  Requirements

  n8n self-hosted or cloud instance  
  Gmail account with OAuth2 authentication  
  Google Sheets with proper structure (3 sheets for CRM, BoM, and Pricing data)  
  Google Gemini API key for AI processing  
  Basic understanding of Google Cloud Console for OAuth setup  

  How to set up

  Import the workflow into your n8n instance  
  Create three Google Sheets with the following column structure:

     CRM Sheet: Email, ProjectCode, CustomerName  
     Bill of Materials: ProjectCode, PartCode, PartDescription, Quantity  
     Pricing Sheet: PartCode, UnitPriceEUR, PartDescription  

  Configure credentials:
     Set up Gmail OAuth2 in Google Cloud Console  
     Configure Google Sheets OAuth2 (can use same project)  
     Get your Google Gemini API key from Google AI Studio  

  Update the workflow:
     Replace placeholder Sheet IDs in the CRM, BoM, and Pricing nodes  
     Adjust company name in the AI Agent’s system message  
     Modify keyword detection if needed  

  Test with a sample email before activating  

  How to customize the workflow

  Add more languages**: Update the keyword detection node with additional terms  
  Modify the quote template**: Edit the HTML in the AI Agent's message to match your branding  
  Change data sources**: Replace Google Sheets with PostgreSQL or MySQL nodes  
  Add approval steps**: Insert a manual approval node for quotes above a certain value  
  Include attachments**: Add PDF or product spec file nodes  
  Enhance notifications**: Add Slack or Teams notifications after quote is sent  
  Implement follow-ups**: Create a separate workflow for reminder emails  

  This template provides a solid foundation for automating your quotation process, while staying flexible to fit your specific business needs.

  Feel free to contact me for further implementation guidelines:
  LinkedIn: Berke

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.gmail","n8n-nodes-base.if","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.googleSheetsTool","n8n-nodes-base.googleSheetsTool","n8n-nodes-base.googleSheetsTool","@n8n/n8n-nodes-langchain.toolCalculator","n8n-nodes-base.gmail","n8n-nodes-base.gmail"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","data processing","sales","customer support"],"complexity":"medium","use_cases":["Automate generation and sending of spare-parts price quotes","Multilingual inbound email handling and replies","Lookup pricing and bill-of-materials from Google Sheets","Extract project/part codes from emails and calculate totals","Standardize professional HTML quote formatting and branding"],"estimated_setup_minutes":60,"required_services":["n8n (self-hosted or cloud)","Gmail account with OAuth2","Google Sheets (3 sheets: CRM, BoM, Pricing)","Google Gemini API (Google AI Studio)","Google Cloud Console (for OAuth credential setup)"],"key_features":["Monitors Gmail for spare-parts requests","AI-driven language detection and extraction of project/part codes","Queries three Google Sheets for CRM, BoM and pricing data","Line-item and grand-total calculations","Generates professional HTML quotes in the sender's language and sends personalized replies via Gmail','Supports multilingual keyword detection and customizable quote templates"],"target_audience":["Sales teams handling spare-parts inquiries","Customer service/support teams","Operations/procurement teams managing BOMs and pricing catalogs」「n8n implementers or automation engineers configuring workflows"]}


name: PDF Document Filling and Generation Server for AI Agents with doqs.dev API
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-06-30T20:34:58.197Z
updated_at: 2025-06-30T20:34:58.197Z
url: https://n8n.io/workflows/5494
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  Complete MCP server exposing 14 doqs.dev | PDF filling API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add doqs.dev | PDF filling API credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the doqs.dev | PDF filling API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.doqs.dev/v1
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (14 total)

  🔧 Designer (7 endpoints)

  • GET /designer/templates/: List Templates
  • POST /designer/templates/: Create Template
  • POST /designer/templates/preview: Preview
  • DELETE /designer/templates/{id}: Delete
  • GET /designer/templates/{id}: List Templates
  • PUT /designer/templates/{id}: Update Template
  • POST /designer/templates/{id}/generate: Generate Pdf

  🔧 Templates (7 endpoints)

  • GET /templates: List 
  • POST /templates: Create
  • DELETE /templates/{id}: Delete 
  • GET /templates/{id}: Get Template
  • PUT /templates/{id}: Update
  • GET /templates/{id}/file: Get File
  • POST /templates/{id}/fill: Fill

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native doqs.dev | PDF filling API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool"]

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metadata_json:
  {"categories":["integration","automation","document generation","AI agent orchestration"],"complexity":"medium","use_cases":["Automated PDF/template generation and filling","Expose doqs.dev PDF operations to AI agents via MCP","Template management (create, update, list, delete, preview)","Integrating AI agents with document workflows (Claude, Cursor, custom apps)","Rapid prototyping of document automation endpoints for apps or bots"],"estimated_setup_minutes":20,"required_services":["doqs.dev PDF Filling API (https://api.doqs.dev)","n8n instance (publicly reachable webhooks)","AI agent or client that supports MCP (e.g., Claude Desktop, Cursor, custom AI apps)"],"key_features":["MCP-triggered server exposing 14 doqs.dev endpoints (designer + templates)","Automatic parameter population with $fromAI() expressions","Native n8n HTTP Request nodes returning original doqs.dev responses","Supports path/query/body/headers and authentication handling","Built-in n8n error handling and logging for API calls and webhooks"],"target_audience":["Developers building AI-driven document workflows","Automation engineers and integrators","No-code/low-code users using n8n who need PDF/template automation for agents"]}


name: Create a Domains-Index API Server with Full Operation Access for AI Agents
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-06-30T20:35:48.674Z
updated_at: 2025-06-30T20:35:48.674Z
url: https://n8n.io/workflows/5495
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  Complete MCP server exposing 14 Domains-Index API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add Domains-Index API credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the Domains-Index API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to /v1
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (14 total)

  🔧 Domains (9 endpoints)

  • GET /domains/search: Domains Database Search
  • GET /domains/tld/{zone_id}: Get TLD records
  • GET /domains/tld/{zone_id}/download: Download Whole Dataset for TLD
  • GET /domains/tld/{zone_id}/search: Domains Search for TLD
  • GET /domains/updates/added: Get added domains, latest if date not specified
  • GET /domains/updates/added/download: Download added domains, latest if date not specified
  • GET /domains/updates/deleted: Get deleted domains, latest if date not specified
  • GET /domains/updates/deleted/download: Download deleted domains, latest if date not specified
  • GET /domains/updates/list: List of updates

  🔧 Info (5 endpoints)

  • GET /info/api: GET /info/api
  • GET /info/stat/: Returns overall stagtistics
  • GET /info/stat/{zone}: Returns statistics for specific zone
  • GET /info/tld/: Returns overall Tld info
  • GET /info/tld/{zone}: Returns statistics for specific zone

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native Domains-Index API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool"]

workflow_json_compressed:
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metadata_json:
  {"categories":["integration","api-server","ai-integration","automation"],"complexity":"medium","use_cases":["Expose Domains-Index API to AI agents","Provide TLD and domain update queries to AI tools","Build AI tools that query domain datasets","Connect Claude/Cursor/custom AI to domain data","Automate domain monitoring and reporting"],"estimated_setup_minutes":29,"required_services":["n8n instance (public webhook reachable)","Domains-Index API credentials","AI agents or apps (Claude, Cursor, custom)","Optional: tunneling/hosting service (ngrok, reverse proxy)"],"key_features":["MCP trigger serves as AI-accessible server endpoint","Implements 14 Domains-Index API operations (domains + info)","Automatic parameter population via $fromAI() expressions","Native HTTP request nodes returning original API responses","Built-in HTTP error handling and logging in n8n workflows\",\"Easy to extend or add custom logic"],"target_audience":["developers","AI integrators/engineers","data analysts/researchers focused on domain data"]}


name: Extract Email Tasks with Gmail, ChatGPT-4o and Supabase
author_name: Paul Taylor
author_username: ptylr
categories: []
created_at: 2025-06-30T22:21:19.552Z
updated_at: 2025-06-30T22:21:19.552Z
url: https://n8n.io/workflows/5496
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  📩 Gmail → GPT → Supabase | Task Extractor

  This n8n workflow automates the extraction of actionable tasks from unread Gmail messages using OpenAI's GPT API, stores the resulting task metadata in Supabase, and avoids re-processing previously handled emails.

  ✅ What It Does

  Triggers on a schedule to check for unread emails in your Gmail inbox.
  Loops through each email individually using SplitInBatches.
  Checks Supabase to see if the email has already been processed.
  If it's a new email:
     Formats the email content into a structured GPT prompt
     Calls ChatGPT-4o to extract structured task data
     Inserts the result into your emails table in Supabase

  🧰 Prerequisites

  Before using this workflow, you must have:

  An active n8n Cloud or self-hosted instance
  A connected Gmail account with OAuth credentials in n8n
  A Supabase project with an emails table and:
      ALTER TABLE emails ADD CONSTRAINT unique_email_id UNIQUE (email_id);
    An OpenAI API key with access to GPT-4o or GPT-3.5-turbo

  🔐 Required Credentials

  | Name            | Type       | Description                       |
  |-----------------|------------|-----------------------------------|
  | Gmail OAuth     | Gmail      | To pull unread messages           |
  | OpenAI API Key  | OpenAI     | To generate task summaries        |
  | Supabase API    | HTTP       | For inserting rows via REST API   |

  🔁 Environment Variables or Replacements

  Supabase_TaskManagement_URI → e.g., https://your-project.supabase.co
  Supabase_TaskManagement_ANON_KEY → Your Supabase anon key

  These are used in the HTTP request to Supabase.

  ⏰ Scheduling / Trigger

  Triggered using a Schedule node
  Default: every X minutes (adjust to your preference)
  Uses a Gmail API filter: unread emails with label = INBOX

  🧠 Intended Use Case

  &gt; Designed for productivity-minded professionals who want to extract, summarize, and store actionable tasks from incoming email — without processing the same email twice or wasting GPT API credits.

  This is part of a larger system integrating GPT, calendar scheduling, and optional task platforms (like ClickUp).

  📦 Output (Stored in Supabase)

  Each processed email includes:
  email_id
  subject
  sender
  received_at
  body (email snippet)
  gpt_summary (structured task)
  requires_deep_work (from GPT logic)
  deleted (initially false)

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.httpRequest","n8n-nodes-base.gmail","n8n-nodes-base.supabase","n8n-nodes-base.if","n8n-nodes-base.splitInBatches","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 4800 characters]

metadata_json:
  {"categories":["automation","email processing","integration","data processing","productivity"],"complexity":"medium","use_cases":["Extract actionable tasks from unread Gmail messages","Email triage and summarization","Automatically populate a task database from email","Prevent duplicate processing of emails","Save GPT-extracted metadata for downstream task workflows"],"estimated_setup_minutes":30,"required_services":["Gmail (OAuth)","OpenAI (GPT-4o or GPT-3.5-turbo)","Supabase (REST/Postgres)","n8n Cloud or self-hosted instance"],"key_features":["Scheduled polling of unread Gmail messages","Batch processing per email (SplitInBatches)","Deduplication check against Supabase (unique email_id)","Formats email into a structured GPT prompt","Calls ChatGPT to extract structured task data (gpt_summary, requires_deep_work) and parses response into fields (email_id, subject, sender, received_at, body)"],"target_audience":["Productivity-minded professionals","Developers/automation engineers implementing integrations","Operations teams or admins who manage email-to-task workflows"]}


name: JavaScriptSentry: Detect Sensitive Information in JavaScript
author_name: The O Suite
author_username: knute
categories: []
created_at: 2025-06-30T22:42:16.224Z
updated_at: 2025-06-30T22:42:16.224Z
url: https://n8n.io/workflows/5497
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  This workflow contains community nodes that are only compatible with the self-hosted version of n8n. 

  How It works:

  When the user clicks "Execute Workflow" they are prompted for a URL, from there the Puppeteer node extracts JavaScript links from the provided URL. 

  The links are then filtered down to ones that are relevant to the original URL that was quieried. 

  From there an AI Agent searches the provided JavaScript links for API Keys, email addresses, and PII leaks.

  Finally a report is generated and sent via gmail to a desired destination. The report contains discovered links as well as any sensitive information that may have been found.

  Why It's Useful:

  This is a great tool for Cybersecurity testing. 
  For Developers, make sure your scripts are clean and you didn't leave anything behind.
  For Ethical Hackers, this is a great tool for Bug Bounties by discovering sensitive information hidden in JavaScript.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.formTrigger","n8n-nodes-puppeteer.puppeteer","n8n-nodes-base.code","n8n-nodes-base.aggregate","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.gmail","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.agent"]

workflow_json_compressed:
  [encoded data, 4444 characters]

metadata_json:
  {"categories":["security","web-scraping","automation","integration"],"complexity":"medium","use_cases":["Detect leaked API keys and credentials in JavaScript files","Website security testing / reconnaissance","Bug bounty reconnaissance","Developer pre-deployment secret checks","PII discovery and compliance checks"],"estimated_setup_minutes":20,"required_services":["n8n self-hosted (required for community nodes)","Puppeteer / headless Chromium access","LLM provider (e.g., OpenAI) for AI agent/lmChat node","Gmail account or SMTP credentials for report delivery","Internet access to crawl target URLs"],"key_features":["Crawl a target page and extract JavaScript links with Puppeteer","Filter JS links relevant to the target domain","AI agent scans JS files for API keys, emails, and PII","Generates a consolidated report of findings","Sends the report via Gmail/email"],"target_audience":["Security engineers / pentesters","Ethical hackers / bug bounty hunters","Developers and devops teams performing secret checks','Compliance and privacy analysts"]}


name: Amazon CloudWatch Application Insights API with 27 Operations
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T04:08:39.781Z
updated_at: 2025-07-01T04:08:39.781Z
url: https://n8n.io/workflows/5498
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  Complete MCP server exposing 27 Amazon CloudWatch Application Insights API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add Amazon CloudWatch Application Insights credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the Amazon CloudWatch Application Insights API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to http://applicationinsights.{region}.amazonaws.com
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (27 total)

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Createapplication (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.CreateApplication: Adds an application that is created from a resource group.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Createcomponent (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.CreateComponent: Creates a custom component by grouping similar standalone instances to monitor.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Createlogpattern (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.CreateLogPattern: Adds an log pattern to a LogPatternSet.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Deleteapplication (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.DeleteApplication: Removes the specified application from monitoring. Does not delete the application.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Deletecomponent (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.DeleteComponent: Ungroups a custom component. When you ungroup custom components, all applicable monitors that are set up for the component are removed and the instances revert to their standalone status.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Deletelogpattern (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.DeleteLogPattern: Removes the specified log pattern from a LogPatternSet.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Describeapplication (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.DescribeApplication: Describes the application.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Describecomponent (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.DescribeComponent: Describes a component and lists the resources that are grouped together in a component.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Describecomponentconfiguration (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.DescribeComponentConfiguration: Describes the monitoring configuration of the component.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Describecomponentconfigurationrecommendation (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.DescribeComponentConfigurationRecommendation: Describes the recommended monitoring configuration of the component.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Describelogpattern (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.DescribeLogPattern: Describe a specific log pattern from a LogPatternSet.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Describeobservation (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.DescribeObservation: Describes an anomaly or error with the application.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Describeproblem (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.DescribeProblem: Describes an application problem.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Describeproblemobservations (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.DescribeProblemObservations: Describes the anomalies or errors associated with the problem.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Listapplications (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.ListApplications: Lists the IDs of the applications that you are monitoring. 

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Listcomponents (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.ListComponents: Lists the auto-grouped, standalone, and custom components of the application.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Listconfigurationhistory (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.ListConfigurationHistory:  Lists the INFO, WARN, and ERROR events for periodic configuration updates performed by Application Insights. Examples of events represented are:    INFO: creating a new alarm or updating an alarm threshold.   WARN: alarm not created due to insufficient data points used to predict thresholds.   ERROR: alarm not created due to permission errors or exceeding quotas.   

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Listlogpatternsets (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.ListLogPatternSets: Lists the log pattern sets in the specific application.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Listlogpatterns (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.ListLogPatterns: Lists the log patterns in the specific log LogPatternSet.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Listproblems (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.ListProblems: Lists the problems with your application.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Listtagsforresource (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.ListTagsForResource: Retrieve a list of the tags (keys and values) that are associated with a specified application. A tag is a label that you optionally define and associate with an application. Each tag consists of a required tag key and an optional associated tag value. A tag key is a general label that acts as a category for more specific tag values. A tag value acts as a descriptor within a tag key.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Tagresource (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.TagResource: Add one or more tags (keys and values) to a specified application. A tag is a label that you optionally define and associate with an application. Tags can help you categorize and manage application in different ways, such as by purpose, owner, environment, or other criteria.  Each tag consists of a required tag key and an associated tag value, both of which you define. A tag key is a general label that acts as a category for more specific tag values. A tag value acts as a descriptor within a tag key.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Untagresource (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.UntagResource: Remove one or more tags (keys and values) from a specified application.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Updateapplication (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.UpdateApplication: Updates the application.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Updatecomponent (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.UpdateComponent: Updates the custom component name and/or the list of resources that make up the component.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Updatecomponentconfiguration (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.UpdateComponentConfiguration: Updates the monitoring configurations for the component. The configuration input parameter is an escaped JSON of the configuration and should match the schema of what is returned by DescribeComponentConfigurationRecommendation. 

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Updatelogpattern (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.UpdateLogPattern: Adds a log pattern to a LogPatternSet.

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native Amazon CloudWatch Application Insights API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
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workflow_json_compressed:
  [encoded data, 8380 characters]

metadata_json:
  {"categories":["integration","cloud monitoring","automation","developer tools"],"complexity":"medium","use_cases":["Expose Amazon CloudWatch Application Insights API to AI agents (MCP)","Automate monitoring tasks: create/update/delete applications, components, log patterns","Query application health: list problems, observations, configuration history","Manage tags and resource metadata at scale","Embed CloudWatch Application Insights operations into AI-driven workflows and custom apps"],"estimated_setup_minutes":5,"required_services":["n8n instance with MCP trigger","Amazon CloudWatch Application Insights (AWS)","AWS IAM credentials with permissions for Application Insights API","Publicly reachable webhook URL or tunneling for AI agent access"],"key_features":["Implements 27 CloudWatch Application Insights operations (create/update/delete/describe/list/tag/untag)","MCP-compatible server endpoint for AI agents","Automatic parameter population via $fromAI() expressions","HTTP request nodes map directly to AWS API endpoints","Native API responses returned to agents with built-in HTTP error handling and logging (n8n)"],"target_audience":["Developers integrating AWS monitoring into AI workflows","DevOps / SRE teams automating monitoring operations","AI builders who need direct access to monitoring APIs as tools/agents (e.g., Claude, Cursor)"]}


name: Expose AWS Budgets API Operations to AI Agents via MCP Server
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T04:29:18.993Z
updated_at: 2025-07-01T04:29:18.993Z
url: https://n8n.io/workflows/5499
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator?

  Join the community

  Complete MCP server exposing 23 AWS Budgets API operations to AI agents.

  ⚡ Quick Setup

  Import this workflow into your n8n instance
  Credentials Add AWS Budgets credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the AWS Budgets API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://budgets.amazonaws.com
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (23 total)

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Createbudget (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.CreateBudget: Creates a budget and, if included, notifications and subscribers.  &lt;important&gt; Only one of BudgetLimit or PlannedBudgetLimits can be present in the syntax at one time. Use the syntax that matches your case. The Request Syntax section shows the BudgetLimit syntax. For PlannedBudgetLimits, see the Examples section.  &lt;/important&gt;

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Createbudgetaction (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.CreateBudgetAction:  Creates a budget action. 

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Createnotification (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.CreateNotification: Creates a notification. You must create the budget before you create the associated notification.

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Createsubscriber (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.CreateSubscriber: Creates a subscriber. You must create the associated budget and notification before you create the subscriber.

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Deletebudget (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DeleteBudget: Deletes a budget. You can delete your budget at any time. &lt;important&gt; Deleting a budget also deletes the notifications and subscribers that are associated with that budget. &lt;/important&gt;

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Deletebudgetaction (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DeleteBudgetAction:  Deletes a budget action. 

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Deletenotification (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DeleteNotification: Deletes a notification. &lt;important&gt; Deleting a notification also deletes the subscribers that are associated with the notification. &lt;/important&gt;

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Deletesubscriber (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DeleteSubscriber: Deletes a subscriber. &lt;important&gt; Deleting the last subscriber to a notification also deletes the notification. &lt;/important&gt;

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Describebudget (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DescribeBudget: Describes a budget. &lt;important&gt; The Request Syntax section shows the BudgetLimit syntax. For PlannedBudgetLimits, see the Examples section.  &lt;/important&gt;

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Describebudgetaction (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DescribeBudgetAction:  Describes a budget action detail. 

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Describebudgetactionhistories (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DescribeBudgetActionHistories:  Describes a budget action history detail. 

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Describebudgetactionsforaccount (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DescribeBudgetActionsForAccount:  Describes all of the budget actions for an account. 

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Describebudgetactionsforbudget (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DescribeBudgetActionsForBudget:  Describes all of the budget actions for a budget. 

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Describebudgetnotificationsforaccount (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DescribeBudgetNotificationsForAccount:  Lists the budget names and notifications that are associated with an account. 

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Describebudgetperformancehistory (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DescribeBudgetPerformanceHistory: Describes the history for DAILY, MONTHLY, and QUARTERLY budgets. Budget history isn't available for ANNUAL budgets.

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Describebudgets (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DescribeBudgets: Lists the budgets that are associated with an account. &lt;important&gt; The Request Syntax section shows the BudgetLimit syntax. For PlannedBudgetLimits, see the Examples section.  &lt;/important&gt;

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Describenotificationsforbudget (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DescribeNotificationsForBudget: Lists the notifications that are associated with a budget.

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Describesubscribersfornotification (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DescribeSubscribersForNotification: Lists the subscribers that are associated with a notification.

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Executebudgetaction (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.ExecuteBudgetAction:  Executes a budget action. 

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Updatebudget (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.UpdateBudget: Updates a budget. You can change every part of a budget except for the budgetName and the calculatedSpend. When you modify a budget, the calculatedSpend drops to zero until Amazon Web Services has new usage data to use for forecasting. &lt;important&gt; Only one of BudgetLimit or PlannedBudgetLimits can be present in the syntax at one time. Use the syntax that matches your case. The Request Syntax section shows the BudgetLimit syntax. For PlannedBudgetLimits, see the Examples section.  &lt;/important&gt;

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Updatebudgetaction (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.UpdateBudgetAction:  Updates a budget action. 

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Updatenotification (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.UpdateNotification: Updates a notification.

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Updatesubscriber (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.UpdateSubscriber: Updates a subscriber.

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native AWS Budgets API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

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workflow_json_compressed:
  [encoded data, 7644 characters]

metadata_json:
  {"categories":["integration","cloud","automation","ai-integration","cost-management"],"complexity":"medium","use_cases":["Allow AI agents to manage and query AWS Budgets","Automate budget creation, updates and notifications","Integrate budget actions and subscribers into AI workflows","Expose AWS Budgets as a tool endpoint for chatbots/agents","FinOps reporting and alerting via AI-driven workflows"],"estimated_setup_minutes":200,"required_services":["n8n instance (with MCP trigger support)","AWS account with Budgets API access and IAM credentials","AI agent or platform that supports MCP (e.g., Claude Desktop, Cursor, custom AI apps)"],"key_features":["Exposes 23 AWS Budgets API operations via MCP endpoint","MCP Trigger acts as server endpoint for AI agents","HTTP Request nodes call https://budgets.amazonaws.com","AI-aware $fromAI() expressions populate parameters automatically","Returns native AWS Budgets API responses to agents','Built-in n8n HTTP error handling and logging','Easily extensible to add custom logic or operations"],"target_audience":["developers","DevOps / Cloud Engineers","FinOps / Finance teams managing cloud spend」「AI builders / chatbot integrators"]}


name: AWS Cost & Usage Report Management for AI Agents
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T05:03:43.730Z
updated_at: 2025-07-01T05:03:43.730Z
url: https://n8n.io/workflows/5502
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:33
description:
  Complete MCP server exposing 4 AWS Cost and Usage Report Service API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add AWS Cost and Usage Report Service credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the AWS Cost and Usage Report Service API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to http://cur.{region}.amazonaws.com
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (4 total)

  🔧 #X-Amz-Target=Awsorigamiservicegatewayservice.Deletereportdefinition (1 endpoints)

  • POST /#X-Amz-Target=AWSOrigamiServiceGatewayService.DeleteReportDefinition: Deletes the specified report.

  🔧 #X-Amz-Target=Awsorigamiservicegatewayservice.Describereportdefinitions (1 endpoints)

  • POST /#X-Amz-Target=AWSOrigamiServiceGatewayService.DescribeReportDefinitions: Lists the AWS Cost and Usage reports available to this account.

  🔧 #X-Amz-Target=Awsorigamiservicegatewayservice.Modifyreportdefinition (1 endpoints)

  • POST /#X-Amz-Target=AWSOrigamiServiceGatewayService.ModifyReportDefinition: Allows you to programatically update your report preferences.

  🔧 #X-Amz-Target=Awsorigamiservicegatewayservice.Putreportdefinition (1 endpoints)

  • POST /#X-Amz-Target=AWSOrigamiServiceGatewayService.PutReportDefinition: Creates a new report using the description that you provide.

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native AWS Cost and Usage Report Service API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

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  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["integration","cloud_cost_management","automation","ai_integration"],"complexity":"medium","use_cases":["Manage AWS Cost & Usage Reports via AI agents","Programmatically create/update/delete/list AWS CUR reports","Expose AWS CUR operations as an MCP tool for AI applications","Automate cost-reporting workflows and FinOps tasks"],"estimated_setup_minutes":5,"required_services":["AWS Cost and Usage Report Service (CUR)","AWS IAM credentials with CUR permissions","n8n instance with MCP capability","AI agents or clients supporting MCP (e.g., Claude Desktop, Cursor, custom AI apps)"],"key_features":["MCP trigger exposing AWS CUR API to AI agents","Implements four CUR operations: PutReportDefinition, ModifyReportDefinition, DeleteReportDefinition, DescribeReportDefinitions","Automatic parameter population via $fromAI() expressions","Native HTTP request handling and error management in n8n","Returns native AWS CUR responses to agents (no extra mapping)"],"target_audience":["Developers","Cloud/Infrastructure engineers","FinOps teams and cost analysts」「AI app builders and integrators"]}


name: Generate Blogs with GPT-4o Prompt Chaining: Outline, Evaluate & Publish to Sheets
author_name: Rahul Joshi
author_username: rahul08
categories: []
created_at: 2025-07-01T06:25:18.849Z
updated_at: 2025-07-01T06:25:18.849Z
url: https://n8n.io/workflows/5504
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  Description

  This n8n automation template delivers a full-stack AI content pipeline designed for marketing teams, content creators, SaaS founders, and growth hackers. It combines prompt chaining, GPT-4o agents, and Google Sheets to generate engaging, SEO-friendly blogs—end to end.

  What This Template Does:

  📝 Generates blog topic ideas using a domain-specific AI agent (e.g., for Sparrow API testing)
  📄 Creates a blog outline with key sections and headings
  ✅ Evaluates & refines the outline to ensure clarity, flow, and engagement
  🧾 Writes the full blog content in structured, long-form paragraphs
  📥 Appends the blog to Google Sheets with the current date

  Built With:
  GPT-4o (via Azure OpenAI)
  LangChain Agents for task-specialized prompt chaining
  Google Sheets integration for automatic publishing
  Schedule Trigger for periodic content generation

  Ideal Use Cases:

  SaaS teams looking to scale inbound content
  API platforms (like Sparrow) publishing technical how-tos
  SEO agencies automating client blog content
  Solo founders growing product visibility via thought leadership

nodes_used:
  ["@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatAzureOpenAi","@n8n/n8n-nodes-langchain.lmChatAzureOpenAi","@n8n/n8n-nodes-langchain.lmChatAzureOpenAi","n8n-nodes-base.scheduleTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatAzureOpenAi","n8n-nodes-base.googleSheets"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","content creation","integration","marketing","seo"],"complexity":"medium","use_cases":["Automated blog topic ideation","Generate SEO-friendly outlines and long-form posts","Refine and evaluate content quality with AI","Publish and track generated content in Google Sheets","Scheduled periodic content generation"],"estimated_setup_minutes":30,"required_services":["Azure OpenAI (GPT-4o)","LangChain agents or equivalent prompt-chaining runtime","Google Sheets API / Google account","n8n instance (with Schedule node)"],"key_features":["Domain-specific AI agent for topic ideation","Prompt chaining via LangChain agents","Outline generation and automated evaluation/refinement","Long-form blog drafting by GPT-4o","Automatic appending to Google Sheets with timestamped entries','Scheduled/recurring content generation"],"target_audience":["Marketing teams","Content creators and SEO agencies","SaaS founders and product teams publishing technical content (API docs/how-tos) of growth hackers and solo founders"]}


name: Capture Website Form Submissions to Notion CRM Database
author_name: David Olusola
author_username: dae221
categories: []
created_at: 2025-07-01T06:44:09.863Z
updated_at: 2025-07-01T06:44:09.863Z
url: https://n8n.io/workflows/5505
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  ⚡️ How It Works

  This workflow captures form submissions from your website, formats the data, and automatically creates a new entry in your Notion CRM database. It eliminates manual copy-pasting and keeps your leads or requests organised in one place.

  🛠 Setup Steps
  	Webhook Node
  	•	Create a webhook in n8n.
  	•	Connect your website form to POST submissions to this webhook URL.
  	Code Node
  	•	Formats the incoming data to match your Notion database structure.
  	•	You can customise the fields in the code to suit your specific form inputs.
  	Notion Node (Create Page)
  	•	Connect your Notion account.
  	•	Choose your target database.
  	•	Map each field from the Code node output to your Notion database properties.
  	Test
  	•	Submit a test form entry.
  	•	Confirm the data appears correctly in Notion.

  ⸻

  👥 Who It’s For

  ✅ Freelancers collecting project inquiries
  ✅ Agencies managing client onboarding forms
  ✅ Business owners wanting organised lead capture
  ✅ Teams that use Notion as their central CRM or task manager
  ✅ Anyone tired of manually transferring form data into Notion

nodes_used:
  ["n8n-nodes-base.webhook","n8n-nodes-base.code","n8n-nodes-base.notion","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["integration","automation","data processing"],"complexity":"simple","use_cases":["Capture website lead or contact form submissions","Automatically create CRM entries in Notion","Streamline client onboarding data collection","Centralise form responses into a Notion database","Eliminate manual copy-paste between form and CRM"],"estimated_setup_minutes":15,"required_services":["Notion (API / workspace)","Website form or any service that can POST to a webhook","n8n (cloud or self-hosted)"],"key_features":["Receive form POSTs via webhook","Format and map incoming form fields with a Code node","Create pages/rows in a Notion database (CRM) automatically","Customisable field mapping to match Notion properties","Simple test flow to verify submissions"],"target_audience":["freelancers","agencies","small business owners', 'teams using Notion as CRM"]}


name: Auto-Stop Email Sequences Based on Replies with IMAP and Google Sheets
author_name: David Olusola
author_username: dae221
categories: []
created_at: 2025-07-01T07:08:46.874Z
updated_at: 2025-07-01T07:08:46.874Z
url: https://n8n.io/workflows/5506
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:34
description:
  Email Reply Auto-Stopper Workflow

  How It Works
  This n8n automation automatically stops your email sequences when prospects reply, preventing embarrassing follow-ups and maintaining professional outreach.

  The Process:

  Email Monitoring - IMAP trigger watches your inbox for new emails
  Reply Detection - When someone replies, it extracts their email address

  Database Lookup - Searches your Google Sheet prospect list for a match
  Auto-Stop - Updates the prospect's status to "STOP" and logs reply date
  Sequence Prevention - Your email sequences skip contacts with "STOP" status
  Real-World Example:

  You send Email 1 to John@clinic.com
  John replies: "Thanks, but not interested"
  This workflow automatically marks John as "STOP"
  Your follow-up sequences skip John completely
  You look professional, not spammy
  Setup Instructions
  Prerequisites
  n8n instance (cloud or self-hosted)
  Email account with IMAP access (Gmail, Outlook, etc.)
  Google Sheets with your prospect data
  Step 1: Prepare Your Google Sheet
  Your prospect sheet MUST include these columns:

  Email - Prospect email addresses
  Email Sent Status - Current status (ACTIVE, SENT, STOP, etc.)
  Reply Date - When they replied (can be empty initially)
  row_number - Unique identifier for each row
  Sample Sheet Structure:

  | First Name | Email | Email Sent Status | Reply Date | row_number |
  |------------|-------|-------------------|------------|------------|
  | John | john@clinic.com | ACTIVE | | 2 |
  | Sarah | sarah@therapy.com | SENT | | 3 |
  Step 2: Set Up Email Credentials
  In n8n, go to Credentials → Add Credential
  Choose IMAP
  Enter your email settings:
  Host: imap.gmail.com (for Gmail)
  Port: 993
  Username: your-email@gmail.com
  Password: your-app-password
  Secure: Yes
  Gmail Users: Enable 2FA and create an App Password

  Step 3: Set Up Google Sheets Access
  Create Google Sheets OAuth2 credential
  Follow n8n's Google Sheets setup guide
  Grant access to your prospect spreadsheet
  Step 4: Import & Configure Workflow
  Import the workflow JSON
  Email Trigger Node:
  Select your IMAP credential
  Keep "UNSEEN" filter enabled
  Google Sheets Nodes:
  Select your Google Sheets credential
  Choose your prospect spreadsheet
  Select correct sheet tab
  Test the workflow with a test email
  Step 5: Connect to Your Email Sequences
  Your existing email automation should check the "Email Sent Status" column:

  Skip sending if status = "STOP"
  Continue sequence if status = "ACTIVE" or "SENT"
  Important Notes
  ⚠️ Critical: Your email sequence automation MUST respect the "Email Sent Status" field, or this won't work.

  💡 Pro Tip: Test with a personal email first to make sure the matching works correctly.

  🔧 Customization: You can modify the "STOP" status to anything that fits your workflow (REPLIED, UNSUBSCRIBED, etc.).

  Use Cases
  Cold email campaigns
  Sales follow-up sequences
  Newsletter nurture flows
  Event promotion follow-ups
  Customer onboarding sequences
  Troubleshooting
  Emails not being detected:

  Check IMAP credentials
  Verify "UNSEEN" filter is working
  Test with manual email trigger
  Sheet not updating:

  Confirm Google Sheets permissions
  Check column names match exactly
  Verify row_number column exists
  No matches found:

  Email addresses must match exactly (case-insensitive)
  Check for extra spaces in email data
  Verify prospect is in the correct sheet tab
  This automation prevents spam complaints, protects sender reputation, and keeps your outreach professional. Set it once, forget it forever.

nodes_used:
  ["n8n-nodes-base.emailReadImap","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.googleSheets"]

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metadata_json:
  {"categories":["automation","email outreach","integration"],"complexity":"medium","use_cases":["Stop follow-up sequences when prospects reply","Avoid sending duplicate/unwanted outreach","Sales cold-email campaigns and SDR workflows","Newsletter or event follow-up suppression","Maintain sender reputation by honoring replies"],"estimated_setup_minutes":20,"required_services":["n8n (cloud or self-hosted)","Email account with IMAP access (Gmail, Outlook, etc.)","Google Sheets (Google account)"],"key_features":["IMAP inbox monitoring for new/reply emails","Extracts sender email and detects replies","Searches prospect list in Google Sheets","Updates prospect row status to STOP and logs reply date","Prevents downstream email sequences from sending to stopped contacts (status-based)"],"target_audience":["sales reps / SDRs","growth marketers","founders doing outreach / agencies running campaigns for clients as well as operations/marketing automation engineers"]}


name: Analyze Images from Forms using GPT-4o-mini Vision and Deliver to Telegram
author_name: David Olusola
author_username: dae221
categories: []
created_at: 2025-07-01T07:27:52.589Z
updated_at: 2025-07-01T07:27:52.589Z
url: https://n8n.io/workflows/5507
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:33
description:
  This workflow analyzes images submitted via a form using OpenAI Vision, then delivers the analysis result directly to your Telegram chat.

  ✅ Use case examples:
  	•	Users submit screenshots for instant AI interpretation
  	•	Automated document or receipt analysis with Telegram delivery
  	•	Quick OCR or image classification workflows

  ⸻

  ⚙️ Setup Guide
  	Form Submission Trigger
  	•	Connect your form app (e.g. Typeform, Tally, or n8n’s own webhook form) to the On form submission trigger node.
  	•	Ensure it sends the image file or URL as input.
  	OpenAI Vision Analysis
  	•	In the OpenAI node, select Analyze Image operation.
  	•	Provide your OpenAI API key and configure the prompt to instruct the model on what to analyze (e.g. “Describe this receipt in detail”).
  	Set Telegram Chat ID
  	•	Use this manual node to input your Telegram Chat ID for delivery.
  	•	Alternatively, automate this with a database lookup or user session if building for multiple users.
  	Telegram Delivery Node
  	•	Connect your Telegram Bot to n8n using your bot token.
  	•	Set up the sendMessage operation, using the analysis result from the previous node as the message text.
  	Testing
  	•	Click Execute workflow.
  	•	Submit an image via your form and confirm it delivers to your Telegram as expected.

nodes_used:
  ["n8n-nodes-base.formTrigger","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.set","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","image-processing","ai"],"complexity":"medium","use_cases":["Automated OCR or receipt extraction from form-submitted images","Instant screenshot interpretation for support or moderation","Image classification or document analysis with results sent to Telegram"],"estimated_setup_minutes":15,"required_services":["OpenAI (GPT-4o-mini Vision) API","Telegram Bot (bot token & chat ID)","Form provider (Typeform, Tally, or n8n webhook)","n8n instance"],"key_features":["Analyze images using OpenAI Vision (customizable prompt)","Deliver analysis results directly to a Telegram chat","Accepts image file uploads or URLs from forms","Simple flow with form trigger → OpenAI analysis → Telegram delivery","Supports manual or automated chat ID lookup for multi-user workflows"],"target_audience":["Developers","Automation engineers","Customer support teams/ops staff who need quick image analysis delivery to chat apps"]}


name: Create a Private Document Q&A System with Llama3, Postgres, Qdrant and Google Drive
author_name: David Olusola
author_username: dae221
categories: []
created_at: 2025-07-01T07:42:55.735Z
updated_at: 2025-07-01T07:42:55.735Z
url: https://n8n.io/workflows/5508
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:33
description:
  ⚙️ How It Works: LocalRAG.AI

  ⚠️ Note: This system only works for self-hosted n8n instances. It will not function on n8n.cloud or other remote setups.
  LocalRAG.AI is a private, on-prem AI assistant that uses your own documents to answer questions intelligently. It combines LangChain, Ollama, Qdrant, and Postgres into a powerful AI pipeline — all running locally for maximum data privacy.

  🔄 What It Does
  Monitors Your Google Drive Folders for new or updated files.
  Downloads the file, extracts the text, and prepares it.
  Generates Embeddings using your local Ollama model (e.g., LLaMA 3).
  Stores them in Qdrant, your local vector database.
  During a chat, it:
  Uses vector search to retrieve relevant chunks.
  Combines them with chat history stored in Postgres.
  Responds via a LangChain AI agent using your local model.
  🛠️ Setup Steps (Self-hosted Only)
  Install and Self-host n8n (e.g., via Docker).
  Set up your Ollama instance locally and load your desired LLM (e.g., llama3).
  Deploy Qdrant locally for vector storage.
  Connect a Postgres DB to store chat history.
  Create and import the workflow in n8n.
  Authenticate Google Drive to monitor folders.
  Connect credentials for Ollama, Qdrant, Postgres in the n8n workflow.
  Start chatting through the Webhook Trigger or custom UI.
  🧠 Perfect For:
  Research teams handling confidential data
  Internal documentation Q&A
  AI chatbots that don’t rely on OpenAI or cloud

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOllama","@n8n/n8n-nodes-langchain.memoryPostgresChat","@n8n/n8n-nodes-langchain.vectorStoreQdrant","@n8n/n8n-nodes-langchain.lmOllama","@n8n/n8n-nodes-langchain.embeddingsOllama","@n8n/n8n-nodes-langchain.toolVectorStore","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.set","n8n-nodes-base.googleDrive","n8n-nodes-base.extractFromFile","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","@n8n/n8n-nodes-langchain.embeddingsOllama","@n8n/n8n-nodes-langchain.vectorStoreQdrant","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","data processing","AI/ML","self-hosted"],"complexity":"complex","use_cases":["Private document question-answering (on-prem)","Internal documentation search and chatbots","Research teams querying confidential corpora","Knowledge base augmentation for product/support","Compliance-sensitive AI assistants that avoid cloud LLMs"],"estimated_setup_minutes":180,"required_services":["n8n (self-hosted)","Ollama (local LLM, e.g., Llama 3)","Qdrant (local vector database)","Postgres (chat memory)","Google Drive (file source and monitoring)","LangChain (agent integration)","Webhook or custom UI endpoint"],"key_features":["Monitors Google Drive for new/updated files","Extracts text and prepares document chunks","Generates embeddings with local Ollama model","Stores embeddings in Qdrant for vector search","Retrieves relevant chunks via vector search during chat sessions and combines with Postgres chat history for context-aware responses using a LangChain agent on local LLMs (Llama3 variant)"],"target_audience":["Developers and ML engineers building private RAG systems","Data engineers / SREs operating self-hosted infrastructure","Research teams and analysts handling sensitive data internally"]}


name: AI-Powered Contact Management in Airtable with Natural Language Commands
author_name: David Olusola
author_username: dae221
categories: []
created_at: 2025-07-01T08:01:44.545Z
updated_at: 2025-07-01T08:01:44.545Z
url: https://n8n.io/workflows/5509
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  AI-Powered Airtable Contact Manager

  Overview

  The AI-Powered Airtable Contact Manager is an intelligent n8n workflow that enables AI assistants to seamlessly manage contact data in Airtable through natural language interactions. Using the Model Context Protocol (MCP), this workflow bridges the gap between conversational AI and structured data management.

  How It Works

  This workflow creates a powerful AI-to-database interface that allows users to manage their Airtable contacts through natural language commands. Here's the complete flow:

  1. AI Interaction Layer
  Users interact with an AI assistant using natural language
  Examples: "Add John Doe to contacts", "Find all contacts assigned to Sarah", "Show me contact details for ID xyz"

  2. MCP Server Trigger
  The AI assistant processes the user's request and identifies the needed operation
  Sends structured commands to the n8n workflow via the MCP (Model Context Protocol)
  Acts as the intelligent routing system for all contact operations

  3. Airtable Operations
  The workflow provides four core contact management functions:

  🔍 Get Record: Retrieves specific contact details using a Record ID
  Input: Record ID from AI
  Output: Complete contact information (Name, Email, Assignee, Status)

  ➕ Create Record: Adds new contacts to the database
  Input: Contact details (Name, Email, Assignee)
  Output: New record with auto-generated ID and default status

  🗑️ Delete Record: Removes contacts permanently
  Input: Record ID to delete
  Output: Confirmation of successful deletion

  🔎 Search Records: Finds contacts using flexible criteria
  Input: Airtable formula for filtering
  Output: All matching contact records

  4. Smart Data Handling
  The workflow uses AI-powered field mapping with $fromAI() functions
  Automatically extracts relevant information from natural language requests
  Maintains data integrity with proper field validation

  Setup Steps

  Prerequisites
  n8n instance (cloud or self-hosted)
  Airtable account with API access
  MCP-compatible AI system

  Step 1: Airtable Preparation
  Create Airtable Base:
     Set up a new base or use existing one
     Note your Base ID (starts with app)

  Set Up Contact Table:
     Create a table with these fields:
       Name (Single line text)
       email (Email)
       Assignee (Single line text)
       Status (Single select: Todo, In progress, Done)
     Note your Table ID (starts with tbl)

  Generate API Token:
     Go to https://airtable.com/developers/web/api/introduction
     Create a personal access token with full permissions
     Save this token securely

  Step 2: n8n Configuration
  Import Workflow:
     Copy the enhanced JSON workflow
     Import into your n8n instance

  Configure Airtable Credentials:
     Go to Credentials in n8n
     Create new "Airtable Personal Access Token" credential
     Enter your Airtable API token
     Name it "full access" (or update credential references in workflow)

  Update Base and Table IDs:
     Replace YOUR_AIRTABLE_BASE_ID with your actual Base ID (starts with app)
     Replace YOUR_AIRTABLE_TABLE_ID with your actual Table ID (starts with tbl)
     Update in all four Airtable nodes

  Update Credential References:
     Replace your-airtable-credential-id with your actual credential ID
     Or rename your credential to match "Airtable API Token"

  Step 3: MCP Integration
  Configure MCP Server:
     Set up your MCP server to communicate with n8n
     Replace your-webhook-path-here and your-webhook-id-here with your actual webhook details
     Configure your AI system to use this workflow

  Update Node IDs (Optional):
     The workflow uses placeholder node IDs for privacy
     n8n will auto-generate new IDs when you import
     No action needed unless you're referencing specific nodes

  Test the Integration:
     Activate the workflow in n8n
     Test each operation through your AI interface
     Verify data flows correctly between AI and Airtable

  Step 4: Customization (Optional)
  Add More Fields:
     Extend the Airtable schema as needed
     Update the Create Record node field mappings
     Modify the Search Record filters

  Enhanced Error Handling:
     Add error handling nodes
     Set up notifications for failed operations
     Implement retry logic for reliability

  Usage Examples

  Once set up, users can interact with the system naturally:

  Creating Contacts:
  "Add Sarah Johnson with email sarah@company.com, assign to Mike"
  "Create a new contact for David Wilson, email david@startup.io"

  Finding Contacts:
  "Show me all contacts assigned to Jennifer"
  "Find contacts with status 'In progress'"
  "Search for contacts with gmail addresses"

  Managing Records:
  "Get details for contact rec123ABC"
  "Delete the contact with ID rec456DEF"
  "Update John's status to Done"

  Benefits

  Natural Language Interface**: No technical knowledge required
  Automated Data Entry**: Reduces manual work and errors
  Flexible Searching**: Find contacts using any criteria
  AI-Powered**: Leverages advanced language understanding
  Scalable**: Easily extend with more operations
  Integrated**: Works seamlessly with existing Airtable workflows

  Technical Notes

  Uses n8n's $fromAI() function for intelligent data extraction
  Implements MCP for standardized AI-to-automation communication
  Supports Airtable's formula syntax for complex searches
  Maintains security through proper credential management
  Designed for high reliability with error handling capabilities

  This workflow transforms contact management from a manual, time-consuming task into an effortless, conversational experience powered by AI.

nodes_used:
  ["@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.airtableTool","n8n-nodes-base.airtableTool","n8n-nodes-base.airtableTool","n8n-nodes-base.airtableTool","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","data processing","AI/ML"],"complexity":"medium","use_cases":["Natural-language contact creation and updates in Airtable","Search and retrieve contacts with flexible criteria","Delete and manage contact records via AI assistant","AI-driven field mapping and data extraction","Integrating conversational AI with Airtable-backed workflows"],"estimated_setup_minutes":45,"required_services":["n8n instance (cloud or self-hosted)","Airtable account with API/Personal Access Token","MCP-compatible AI system or LLM provider","Accessible MCP/webhook endpoint for n8n"],"key_features":["Natural language interface for contact operations (create/get/search/delete)","Model Context Protocol (MCP) integration to route structured commands","AI-powered field mapping using $fromAI() for parsing user input","Supports Airtable formula-based searches for complex filters","Credential and webhook configuration guidance with optional error handling and retries"],"target_audience":["Developers building AI-to-database integrations","No-code/low-code users automating CRM tasks","Small business operators and sales/marketing teams managing contacts at scale\",\"Automation engineers and integrators"]}


name: Monitor Dynamic Website Changes with Firecrawl, Sheets & Gmail Alerts
author_name: Franz
author_username: agents-by-franz
categories: []
created_at: 2025-07-01T08:14:05.491Z
updated_at: 2025-07-01T08:14:05.491Z
url: https://n8n.io/workflows/5510
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  🕸️ Dynamic Website Change Monitor with Smart Email Alerts

  Never miss important website updates again! This workflow automatically tracks changes on dynamic websites (think React apps, JavaScript-heavy sites) and sends you instant email notifications when something changes. Perfect for keeping tabs on competitors, monitoring product updates, or staying on top of important announcements.

  ✨ What makes this special?

  🚀 Handles Dynamic Websites: Uses Firecrawl API to scrape JavaScript-rendered content that basic scrapers can't touch
  📧 Smart Email Alerts: Only sends notifications when content actually changes (no spam!)
  📊 Historical Tracking: Keeps a complete log of all changes in Google Sheets
  🛡️ Bulletproof: Continues working even if one part fails
  ⚡ Ready to Deploy: Webhook-triggered, perfect for cron jobs or external schedulers

  🎯 Perfect for monitoring:
  Competitor pricing pages
  Job board postings
  Product availability updates  
  News sites for breaking stories
  API documentation changes
  Terms of service updates

  🛠️ What you'll need to get started:

  API Accounts & Keys:
  Firecrawl Account 🔥
     Sign up at firecrawl.dev
     Grab your API key from the dashboard
     Create a "Bearer Auth" credential in n8n

  Google Cloud Setup ☁️
     Enable Google Sheets API
     Enable Gmail API  
     Set up OAuth2 credentials
     Add both as credentials in n8n

  Google Sheets Document 📋
     Create a new spreadsheet
     Add two tabs: "Log" and "comparison"
     Follow the structure outlined in the workflow notes

  🚀 How it works:

  Webhook receives trigger → Starts the monitoring process
  Firecrawl scrapes website → Gets fresh content (even JavaScript-rendered!)
  Smart comparison → Checks against previously stored content
  Change detected? → If yes, send email + log everything
  Update storage → Prepares for next monitoring cycle

  ⚙️ Setup Steps:

  Import this workflow into your n8n instance
  Configure credentials for Firecrawl, Google Sheets, and Gmail
  Update the target URL in the Firecrawl node
  Set your email address in the Gmail node
  Create your Google Sheets with the required structure
  Test it manually first, then activate!

  🎨 Customize it your way:

  Target any website** by updating the URL
  Change email templates** to match your style
  Adjust monitoring frequency** with external cron jobs
  Switch between markdown/HTML** extraction formats
  Fine-tune change detection** sensitivity

  🔧 Troubleshooting:

  Firecrawl errors?** Check your API key and rate limits
  Google Sheets issues?** Verify OAuth permissions and sheet structure  
  Email not sending?** Check Gmail API quotas and spam folders
  Webhook problems?** Make sure the workflow is activated

  Ready to never miss another website change? Let's get this automation running! 🎉

nodes_used:
  ["n8n-nodes-base.webhook","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.if","n8n-nodes-base.respondToWebhook","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.gmail","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.respondToWebhook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 7624 characters]

metadata_json:
  {"categories":["automation","web monitoring","web scraping","notifications","data logging','integrations"],"complexity":"medium","use_cases":["Competitor pricing monitoring","Job board / listing updates","Product availability tracking","News / breaking story alerts","API documentation change detection','Terms of service or legal page monitoring"],"estimated_setup_minutes":45,"required_services":["Firecrawl API (for JS-rendered scraping)","Google Sheets API / Google Sheets","Gmail API (send alerts)","Google Cloud Project with OAuth2 credentials","n8n instance (workflow runner)","Webhook endpoint or external scheduler (cron)"],"key_features":["Renders and scrapes JavaScript-heavy pages via Firecrawl","Smart diff to only send email when content changes","Logs full history of checks and diffs to Google Sheets","Webhook-triggered — works with cron or external schedulers","Configurable email templates (Gmail) and extraction formats (HTML/markdown)"],"target_audience":["Developers","Product managers","Marketing / competitive intelligence teams','Operations / SREs"]}


name: Multilingual Voice & Text Telegram Bot with ElevenLabs TTS and LangChain Agents
author_name: IvanCore
author_username: ivancore
categories: []
created_at: 2025-07-01T09:21:27.881Z
updated_at: 2025-07-01T09:21:27.881Z
url: https://n8n.io/workflows/5511
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:33
description:
  This workflow contains community nodes that are only compatible with the self-hosted version of n8n. 

  Telegram Voice AI Assistant
  This n8n template creates a multimodal Telegram bot that dynamically responds to users:

  Replies with voice** when receiving voice messages (using ElevenLabs TTS)

  Replies with text** for text-based queries

  Supports custom AI tools (e.g., crypto APIs, databases, or custom functions)

  Built with LangChain Agents, it can integrate any external API or data source into conversations.

  Key Features
  🎙️ Smart Response Logic
  Voice Query? → Voice Reply**
  Transcribes audio via ElevenLabs STT
  Processes with AI (Groq/Gemini)
  Converts text response to natural speech (ElevenLabs TTS)

  Text Query? → Text Reply**
  Bypasses TTS/STT for faster responses

  🛠️ Extensible AI Tools
  Add your own tools:
  Database lookups
  Weather/stock APIs
  Custom Python functions
  RAG (document retrieval)

  Supports multi-step tool chaining (e.g., "Get BTC price → analyze trends → summarize")

  🌐 Language & Context
  Auto-detects user language (via Telegram’s language_code)
  Maintains session memory (remembers conversation history)

  Use Cases
  Voice-first customer support**
  Crypto/analytics assistants** (e.g., "What’s Ethereum’s current gas fee?")
  Multilingual FAQ bots**
  Educational tutors** (voice-interactive learning)

  Requirements
  Telegram Bot Token**
  ElevenLabs API Key** (For TTS/STT)
  Groq API Key* or *Google Gemini API Key**

  Customization Tips
  Change AI personality*: Modify the *systemMessage in the Voice Assistant node
  Add more models**: Swap Groq/Gemini for OpenAI, Anthropic, etc.
  Extend functionality**: Add RAG (Retrieval-Augmented Generation) for document queries

  Take this template to create a Siri-like AI assistant for Telegram in minutes! 🚀

nodes_used:
  ["n8n-nodes-base.telegramTrigger","n8n-nodes-base.set","n8n-nodes-base.switch","n8n-nodes-base.telegram","@elevenlabs/n8n-nodes-elevenlabs.elevenLabs","n8n-nodes-base.set","n8n-nodes-base.aggregate","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.lmChatGroq","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.if","@elevenlabs/n8n-nodes-elevenlabs.elevenLabs","n8n-nodes-base.telegram","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["conversational AI","voice assistant","integration","multimodal"],"complexity":"complex","use_cases":["Voice-first customer support","Multilingual FAQ bot","Educational voice tutors","Crypto/analytics assistant","Multimodal Telegram assistant with tool integrations"],"estimated_setup_minutes":60,"required_services":["n8n self-hosted (community nodes support)","Telegram Bot Token (Telegram API)","ElevenLabs API Key (TTS/STT)","Groq API Key or Google Gemini API Key"],"key_features":["Voice reply for voice messages (ElevenLabs STT -> AI -> ElevenLabs TTS)","Text reply for text queries (skips TTS/STT)","LangChain Agents for extensible tool integration","Support for custom tools: APIs, databases, Python functions","Multi-step tool chaining and RAG (document retrieval) support 
  (integrate external data sources) 
  (note: optional) 
  ) 
  Auto language detection via Telegram language_code 
  Session memory to maintain conversation context"],"target_audience":["Developers building chatbots","AI/ML engineers integrating LLMs and TTS","Product teams creating voice-enabled experiences for Telegram users (B2B/B2C) ) 
  Customer support teams wanting voice-first assistants 
  Educators building interactive voice tutors 
  Crypto/finance analysts creating queryable assistants"]}


name: Send Daily Currency Exchange Rate Updates via CurrencyFreaks API and Gmail
author_name: Sarfaraz Muhammad Sajib
author_username: sarfarazmuhammad
categories: []
created_at: 2025-07-01T10:53:48.034Z
updated_at: 2025-07-01T10:53:48.034Z
url: https://n8n.io/workflows/5512
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:34
description:
  Daily Currency Update Workflow (n8n)

  Trigger: ScheduleTrigger node (configurable interval)
  Set Variables: API Key, Preferred Currencies (PKR, GBP, EUR, USD, BDT, INR)
  HTTP Request: Fetch latest exchange rates from CurrencyFreaks API
  Set Recipient Email
  Set Email Subject
  Send Email: HTML formatted via Gmail OAuth2 with dynamic rate data (date, base currency, rates)
  Flow: ScheduleTrigger → Set API Key & Currencies → HTTP Request → Set Recipient → Set Subject → Gmail Send

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.gmail","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","notifications","reporting"],"complexity":"simple","use_cases":["Daily currency exchange rate reports","Finance and accounting rate monitoring","Automated client or team updates","Treasury / cash management alerts","Travel cost or invoicing preparation"],"estimated_setup_minutes":15,"required_services":["n8n","CurrencyFreaks API (API key)","Gmail account with OAuth2 access"],"key_features":["Scheduled trigger for recurring runs","Fetch latest exchange rates via HTTP request","Configurable list of currencies and base currency","HTML-formatted email with dynamic rate data","Send email via Gmail OAuth2"],"target_audience":["finance teams","accountants","small business owners/administrators responsible for payments or pricing decisions using exchange rates to make decisions or notify stakeholders"]}


name: Automate FAQ Responses with WhatsApp Keyword Detection Bot
author_name: Solido AI
author_username: solidoai
categories: []
created_at: 2025-07-01T14:23:34.677Z
updated_at: 2025-07-01T14:23:34.677Z
url: https://n8n.io/workflows/5516
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  How it works:
  This bot operates in a continuous WhatsApp monitoring loop. It analyzes messages to detect keywords in common questions (like hours, prices, and location) and sends automatic replies with predefined information. For unrecognized questions, it directs the user to manual assistance.

  Set up steps:
  The initial setup involves integrating with the WhatsApp API, registering keywords and their respective responses, and defining the fallback flow. It takes only a few minutes to have the bot running with essential information.

nodes_used:
  ["n8n-nodes-base.whatsAppTrigger","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.switch","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.whatsApp","n8n-nodes-base.whatsApp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","customer support","chatbot"],"complexity":"simple","use_cases":["Automated FAQ replies via WhatsApp","Basic customer support and triage","Providing business info (hours, prices, location)","Escalating unrecognized queries to manual agents"],"estimated_setup_minutes":5,"required_services":["WhatsApp Business API (or provider like Twilio/360dialog)","n8n instance/workflow runner"],"key_features":["Continuous WhatsApp message monitoring","Keyword detection for common questions","Predefined automatic replies","Configurable keyword-response mapping","Fallback flow to route unrecognized queries to manual assistance"],"target_audience":["Small businesses","Customer support teams","Store owners and local services providers (restaurants, shops)"]}


name: Manage Personal Expenses with Webhooks and Google Sheets Automated Tracker
author_name: Solido AI
author_username: solidoai
categories: []
created_at: 2025-07-01T14:24:38.880Z
updated_at: 2025-07-01T14:24:38.880Z
url: https://n8n.io/workflows/5517
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  How it works:
  This system functions by receiving expenses via webhook POST. It validates the data, stores it in Google Sheets, and, daily at 8 PM, generates and sends financial summaries. Automatic categorization simplifies the organization of expenses.

  Set up steps:
  Setup involves creating the Google Sheet, configuring the webhook, and defining the categorization rules. The process is quick and intuitive, taking about 10-15 minutes for the system to be ready to receive your expenses.

nodes_used:
  ["n8n-nodes-base.webhook","n8n-nodes-base.function","n8n-nodes-base.googleSheets","n8n-nodes-base.function","n8n-nodes-base.respondToWebhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.cron","n8n-nodes-base.googleSheets","n8n-nodes-base.function","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","data processing","personal finance"],"complexity":"medium","use_cases":["Capture expenses via webhook POST","Validate and normalize expense data","Store and maintain expense records in Google Sheets","Automatically categorize expenses using rules","Generate and send daily financial summaries at a scheduled time"],"estimated_setup_minutes":15,"required_services":["Google Sheets","Webhook-capable client or service to POST expenses (uses n8n HTTP Webhook)","Optional email or messaging provider/API for sending summaries (SMTP, SendGrid, Slack, etc.)"],"key_features":["Webhook intake for real-time expense submission","Payload validation and normalization","Automatic categorization based on user-defined rules","Persistent storage in Google Sheets","Scheduled daily summary generation (cron at 8 PM) and delivery"],"target_audience":["individuals tracking personal expenses","freelancers and gig workers","small business owners managing simple expense logs」「n8n users and automation builders"]}


name: Automate Gmail Inbox Organization with Rule-based Categorization and Smart Actions
author_name: Solido AI
author_username: solidoai
categories: []
created_at: 2025-07-01T14:25:20.168Z
updated_at: 2025-07-01T14:25:20.168Z
url: https://n8n.io/workflows/5518
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  How it works:
  The organizer continuously monitors your Gmail inbox. It analyzes sender and subject to categorize emails (Work, Purchases, Newsletter) and automatically applies labels. Based on the category, it performs specific actions, such as marking important emails or archiving newsletters.

  Set up steps:
  The initial setup requires Gmail access permission and defining the categorization rules and desired actions for each email type. This process can be configured in approximately 5 to 10 minutes, depending on the complexity of the rules you wish to establish.

nodes_used:
  ["n8n-nodes-base.gmailTrigger","n8n-nodes-base.switch","n8n-nodes-base.gmail","n8n-nodes-base.gmail","n8n-nodes-base.gmail","n8n-nodes-base.gmail","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","email management","productivity","integration"],"complexity":"simple","use_cases":["Automatically categorize incoming emails (Work, Purchases, Newsletter)","Apply labels and folder organization","Archive or mark emails as important based on category","Reduce inbox clutter and improve prioritization","Automate repetitive email triage tasks"],"estimated_setup_minutes":10,"required_services":["Gmail (Google account) / Gmail API"],"key_features":["Continuous inbox monitoring","Rule-based sender and subject analysis","Automatic labeling and categorization","Category-specific actions (archive, mark important, etc.)","Customizable rules and actions"],"target_audience":["individuals","professionals","small business owners / teams looking to streamline email workflow"]}


name: Process Documents with Recursive Chunking using Google Drive, OpenAI & Gemini RAG
author_name: Mohsin Ali
author_username: binbakhsh
categories: []
created_at: 2025-07-01T15:06:26.131Z
updated_at: 2025-07-01T15:06:26.131Z
url: https://n8n.io/workflows/5521
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:33
description:
  1. Document Ingestion & Processing

  Google Drive Trigger monitors for new files → Loop Over Items processes each file → File Info extracts metadata → Google Drive downloads the actual content → Switch routes to appropriate extractors (PDF or TEXT) based on file type

  2. Content Transformation & Chunking

  Document Data node processes extracted text → Recursive Splitter breaks content into contextual chunks → Chunk Splitting applies intelligent segmentation while preserving document context and relationships between chunks

  3. Embedding & Storage

  Basic LLM Chain processes chunks → OpenAI Chat Model generates contextual understanding → Summarize creates document summaries → Supabase Vector Store saves embeddings with metadata → Embeddings OpenAI creates vector representations → Default Data Loader handles storage operations

  4. Query Processing & Retrieval

  When Clicking Execute triggers user queries → OpenAI processes and understands the question → AI Agent orchestrates hybrid search (combining vector similarity + keyword matching) → Google Gemini Chat Model generates final responses using retrieved context → HTTP Request handles additional external data sources

nodes_used:
  ["n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.splitInBatches","n8n-nodes-base.googleDrive","n8n-nodes-base.switch","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.summarize","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.vectorStoreSupabase","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterCharacterTextSplitter","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.set","n8n-nodes-base.extractFromFile","n8n-nodes-base.extractFromFile","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.splitOut","n8n-nodes-base.httpRequestTool"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","data processing","knowledge management"],"complexity":"complex","use_cases":["Build RAG-enabled document search/QA systems","Automated document ingestion and indexing","Enterprise knowledge base creation","Document summarization and metadata extraction","Customer support and contract analysis"],"estimated_setup_minutes":60,"required_services":["Google Drive API","OpenAI (Chat + Embeddings)","Google Gemini (or Vertex AI)","Supabase (vector store)","Optional external HTTP APIs"],"key_features":["Automatic file monitoring and ingestion from Google Drive","File-type routing and extractor selection (PDF / text)","Recursive contextual chunking that preserves document relationships","Embedding generation and storage in Supabase vector DB","Hybrid retrieval (vector similarity + keyword matching) with AI agent orchestration\",\"LLM-based summarization and contextual understanding\",\"Query processing pipeline with multi-model responses (OpenAI + Gemini)"],"target_audience":["Developers","Data engineers","ML engineers / ML Ops practitioners\",\"Knowledge managers and product teams"]}


name: Convert and Manipulate PDFs with Api2Pdf and AWS Lambda
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T17:11:54.298Z
updated_at: 2025-07-01T17:11:54.298Z
url: https://n8n.io/workflows/5522
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  Complete MCP server exposing 9 Api2Pdf - PDF Generation, Powered by AWS Lambda API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community
  Import this workflow into your n8n instance
  Credentials Add Api2Pdf - PDF Generation, Powered by AWS Lambda credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the Api2Pdf - PDF Generation, Powered by AWS Lambda API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://v2018.api2pdf.com
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (9 total)

  🔧 Chrome (3 endpoints)

  • POST /chrome/html: Convert raw HTML to PDF
  • GET /chrome/url: Convert URL to PDF
  • POST /chrome/url: Convert URL to PDF

  🔧 Libreoffice (1 endpoints)

  • POST /libreoffice/convert: Convert office document or image to PDF

  🔧 Merge (1 endpoints)

  • POST /merge: Merge multiple PDFs together

  🔧 Wkhtmltopdf (3 endpoints)

  • POST /wkhtmltopdf/html: Convert raw HTML to PDF
  • GET /wkhtmltopdf/url: Convert URL to PDF
  • POST /wkhtmltopdf/url: Convert URL to PDF

  🔧 Zebra (1 endpoints)

  • GET /zebra: Generate bar codes and QR codes with ZXING.

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native Api2Pdf - PDF Generation, Powered by AWS Lambda API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool"]

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metadata_json:
  {"categories":["automation","integration","document processing","serverless","AI integration"],"complexity":"medium","use_cases":["Convert HTML/URL/documents/images to PDF","Merge multiple PDFs","Generate barcodes/QR codes (Zebra)","Expose PDF generation API as an MCP tool for AI agents","Automate document generation from AI-driven parameters"],"estimated_setup_minutes":200,"required_services":["Api2Pdf (PDF Generation API)","n8n instance with MCP/webhook support","Api2Pdf AWS Lambda backend (managed by Api2Pdf)","AI agents or clients (e.g., Claude, Cursor) for consumption"],"key_features":["Maps 9 Api2Pdf endpoints (Chrome, Wkhtmltopdf, LibreOffice, Merge, Zebra) to MCP endpoints","AI-aware parameter injection via $fromAI() expressions","Native n8n HTTP request handling and error management","Returns original Api2Pdf responses to agents","Quick deploy: add credentials, activate workflow, copy webhook URL"],"target_audience":["Developers and integration engineers","AI builders/agents integrators","Automation and DevOps teams handling document workflows"]}


name: Evaluate tool usage accuracy in multi-agent AI workflows using Evaluation nodes
author_name: Angel Menendez
author_username: djangelic
categories: []
created_at: 2025-07-01T17:12:40.342Z
updated_at: 2025-07-01T17:12:40.342Z
url: https://n8n.io/workflows/5523
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:32
description:
  Who's it for
  This workflow is ideal for AI developers running multi-agent systems in n8n who need to quantitatively evaluate tool usage behavior. If you're building autonomous agents and want to verify their decisions against ground-truth expectations, this workflow gives you plug-and-play observability.

  What it does
  This template uses n8n's built-in Evaluation Trigger and Evaluation nodes to assess whether an AI agent correctly used all the expected tools. It supports:

  Dataset-driven testing of agent behavior

  Logging actual tools to compare them with the expected tools

  Assigning performance metrics (tool_called = true/false)

  Persisting output back to Google Sheets for further debugging

  The workflow can be triggered by either the chat input or the dataset row evaluation. It routes through a multi-tool agent node powered by the best LLMs. The agent has access to tools such as web search, calculator, vector search, and summarizer tools. The workflow then aims to validate tool use decisions by extracting the intermediate steps from the agent (i.e., action + observation) and comparing the tools that were called with the expected tools. If the tools that were called during the workflow execution match, then it's a pass; otherwise, it's documented as a fail. The evaluation nodes take care of that process. 

  How to set it up
  Connect your Google Sheets OAuth2 credential. Replace the document with your own test dataset.

  Set your desired models and configure the different agent tools, such as the summarizer and vector store. The default vector store used is Qdrant, so the user must create this vector store with a few samples of queries + web search results.

  Run from either the chat trigger or the evaluation trigger to test.

  Requirements
  Google Sheets OAuth2 credential

  OpenRouter / OpenAI credentials for AI agents and embeddings

  Firecrawl and Qdrant credentials for web + vector search

  How to customize
  Edit the Search Agent system message to define tool selection behavior

  Add more metric columns in the Evaluation node for complex scoring

  Add new tool nodes and link them to the agent block

  Swap in your own summarizer

nodes_used:
  ["@n8n/n8n-nodes-langchain.toolCalculator","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.set","n8n-nodes-base.noOp","n8n-nodes-base.evaluationTrigger","n8n-nodes-base.evaluation","n8n-nodes-base.evaluation","n8n-nodes-base.evaluation","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.httpRequestTool","@n8n/n8n-nodes-langchain.lmChatOpenRouter","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.vectorStoreQdrant"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","AI evaluation","integration"],"complexity":"complex","use_cases":["Dataset-driven testing of multi-agent behavior","Validating whether agents invoked expected tools","Logging and persisting evaluation results for debugging","Quantitative scoring of agent tool-usage (pass/fail)","Observability for autonomous agent decision-making"],"estimated_setup_minutes":45,"required_services":["Google Sheets (OAuth2)","OpenRouter / OpenAI (LLM + embeddings)","Firecrawl (web search)","Qdrant (vector store)"],"key_features":["Uses n8n Evaluation Trigger and Evaluation nodes for automated scoring","Extracts agent intermediate steps (action + observation) to identify called tools","Compares actual vs expected tools and assigns tool_called true/false metrics","Dataset-driven runs with Google Sheets persistence","Multi-tool agent (web search, calculator, vector search, summarizer) integration supportable via LLMs/agents block','Configurable system message and metric columns for custom scoring"],"target_audience":["AI developers building autonomous/multi-agent systems","ML engineers and QA focused on agent behavior testing","Automation engineers integrating LLM agents with external tools"]}


name: Generate & Email Personalized Jokes with GPT-4o-mini and Gmail
author_name: Oriol Seguí
author_username: oxsr11
categories: []
created_at: 2025-07-01T17:55:38.306Z
updated_at: 2025-07-01T17:55:38.306Z
url: https://n8n.io/workflows/5525
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  This fun workflow automates the generation and delivery of personalized jokes by email based on the names or objects entered in the form. The process works as follows:

  On form submission
  The workflow starts when someone submits a form with the required names or objects to create the joke.
  You can modify the form fields to make the jokes more creative.

  Set the output language
  Manually define the language in which you want to receive the joke.

  OpenAI Message Model
  Uses the OpenAI model to generate the joke based on the prompt and in the chosen language.
  (The response is limited to around 200 tokens.)

  Gmail: send message
  The generated joke is automatically sent to the specified email address via Gmail.

nodes_used:
  ["n8n-nodes-base.formTrigger","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","communication","entertainment"],"complexity":"simple","use_cases":["Automated personalized birthday or celebration emails","Team icebreakers or internal morale boosters","Marketing engagement with humorous outreach","Event follow-ups and attendee engagement","Demo or prototype for GPT-powered email content"],"estimated_setup_minutes":10,"required_services":["OpenAI (GPT-4o-mini)","Gmail (Google account / Gmail API)","n8n (form node or external form provider like Typeform/Webhook)"],"key_features":["Generate personalized jokes from form inputs","Manual selection of output language","Limits model response length (~200 tokens)","Automatically sends joke via Gmail","Customizable prompt and form fields for creativity"],"target_audience":["community managers","HR teams","marketers and growth teams doing outreach experiments or fun campaigns  — [concise: marketers]  (edited)"]}


name: Create a WHOIS API Interface for AI Agents with 8 Domain Management Operations
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T19:19:26.667Z
updated_at: 2025-07-01T19:19:26.667Z
url: https://n8n.io/workflows/5528
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  Complete MCP server exposing 8 Bulk WHOIS API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add Bulk WHOIS API credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the Bulk WHOIS API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to http://localhost:5000
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (8 total)

  🔧 Batch (4 endpoints)

  • GET /batch: Get your batches
  • POST /batch: Create batch. Batch is then being processed until all provided items have been completed. At any time it can be get to provide current status with results optionally.
  • DELETE /batch/{id}: Delete batch
  • GET /batch/{id}: Get batch

  🔧 Db (1 endpoints)

  • GET /db: Query domain database

  🔧 Domains (3 endpoints)

  • GET /domains/{domain}/check: Check domain availability
  • GET /domains/{domain}/rank: Check domain rank (authority).
  • GET /domains/{domain}/whois: WHOIS query for a domain

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native Bulk WHOIS API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool"]

workflow_json_compressed:
  [encoded data, 4100 characters]

metadata_json:
  {"categories":["integration","automation","API","AI integration","domain management"],"complexity":"medium","use_cases":["Expose Bulk WHOIS API to AI agents via an MCP server","Batch WHOIS processing and status tracking","Domain availability and WHOIS lookups for automation","Domain authority/rank checks","Query a local domain database for analytics or enrichment"],"estimated_setup_minutes":20,"required_services":["n8n instance (with MCP trigger support)","Bulk WHOIS API backend (service reachable, example: http://localhost:5000)","Bulk WHOIS API credentials","AI agents or clients (Claude Desktop, Cursor, custom AI apps) — to connect via MCP URL"],"key_features":["MCP trigger exposes 8 WHOIS-related endpoints to AI agents","Batch endpoints: create, list, get, delete and monitor processing status","Domain endpoints: availability check, rank (authority), and WHOIS lookup","Database query endpoint for domain datastore","$fromAI() expressions auto-populate parameters from AI agent requests (path, query, body, headers, auth)"],"target_audience":["developers building AI tools and integrations","AI engineers and researchers integrating tool use","DevOps / SREs deploying agent-accessible endpoints in n8n workflows"]}


name: Monitor Software Compliance with Jamf Patch Summaries in Slack
author_name: Jean-Marie Rizkallah
author_username: mrrobot
categories: []
created_at: 2025-07-01T19:28:28.897Z
updated_at: 2025-07-01T19:28:28.897Z
url: https://n8n.io/workflows/5529
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  🧩 Jamf Patch Summary to Slack
  Stay on top of software patch compliance by automatically posting Jamf patch summaries to Slack.
  This helps IT and security teams quickly identify outdated installs and take action—without logging into Jamf.

  ✅ Prerequisites
  • A Jamf Pro API key with permissions to read software titles and patch summary
  • A Slack app or incoming webhook URL with permission to post messages to your desired channel

  🔍 How it works
  • Manually trigger the flow or Add a webhook
  • Fetch a list of software titles from Jamf Pro
  • Filter to select the software you're tracking (e.g. Chrome, Edge)
  • Retrieve the patch summary for that software (latest version, up-to-date, out-of-date counts)
  • Format the summary into Slack Block Kit
  • Post the formatted summary into a Slack channel

  ⚙️ Set up steps
  • Takes ~5–10 minutes to configure
  • Set your server BaseURL variable in the Set Node
  • Add your Jamf Pro API credentials in the HTTP Request nodes (Get & Retrieve)
  • Set the target software ID in the Filter node
  • Add your Slack webhook URL or token in the final HTTP node
  • Optional: Adjust Slack formatting inside the Function node

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.filter","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.slack"]

workflow_json_compressed:
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metadata_json:
  {"categories":["integration","automation","IT management","security","notifications"],"complexity":"medium","use_cases":["Monitor software patch compliance","Notify IT/security teams of out-of-date installs","Automated reporting of patch summary to Slack","Track specific applications (e.g., Chrome, Edge)"],"estimated_setup_minutes":10,"required_services":["Jamf Pro API (with read permissions for software titles and patch summaries)","Slack app or Incoming Webhook (permission to post to channel)","n8n instance / reachable BaseURL"],"key_features":["Fetch software titles from Jamf Pro","Filter to target specific software","Retrieve patch summary (latest version, up-to-date/out-of-date counts)","Format message using Slack Block Kit","Post formatted summary to Slack channel (webhook or token)"],"target_audience":["IT administrators","Security teams","Desktop engineers/endpoint teams (Sysadmins)"]}


name: Auto Generate & Post LinkedIn Content for Amazon Sellers with GPT-4o & Apify
author_name: Usman Liaqat
author_username: usmanliaqat
categories: []
created_at: 2025-07-01T20:30:56.580Z
updated_at: 2025-07-01T20:30:56.580Z
url: https://n8n.io/workflows/5531
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  How it works

  This powerful automation turns LinkedIn influencer activity into high performing Amazon seller content, fully hands-off. It runs in two phases:

  AI Content Generator
     Retrieves influencer usernames from Airtable
     Scrapes their most recent LinkedIn post via Apify
     Uses OpenAI (GPT-4o) to generate original Amazon focused post content
     Generates a branded LinkedIn image using AI (WeSpark visual identity)
     Saves content & image link to Google Drive
     Creates a record in Airtable with Status = In review

  Scheduled Publisher

     Every day at 4 PM, one post from Airtable with Status = Approved is:

       Downloaded from Drive
       Published on LinkedIn
       Marked as Posted in Airtable

  ⚙️ Set Up Steps

  Time to set up:** 15–20 minutes
  Create two Airtable tables:

    influencers with UserName (LinkedIn usernames only)
    posts with fields:

      Description (text)
      Image (Drive link)
      InfluencerUsername
      InfluencerPost
      InfluencerPostCreatedAt
      Status (In review, Approved, Posted, Rejected)
  Add Google Drive OAuth2 credentials for saving & downloading images
  Configure OpenAI GPT-4o key (for text + image generation)
  Set up Apify LinkedIn scraper and add header credentials
  Link your LinkedIn account (company or personal profile)

  Workflow Phases

  1. AI Generator (Triggered manually)

  Loops over influencer usernames
  Scrapes latest LinkedIn post
  Skips if already stored
  Uses GPT-4o to:

    Rewrite the post uniquely for Amazon sellers
    Generate JSON with text + image prompt
  Saves AI-generated image to Google Drive
  Logs post to Airtable with In review status

  2. Daily Publisher (Scheduled)

  Runs every day at 4 PM
  Filters Airtable for Status = Approved
  Picks 1 record, downloads image from Drive
  Publishes to LinkedIn
  Updates Airtable status to Posted

  Airtable Setup Example

  influencers Table:

  | UserName    |
  | ----------- |
  | jon-derkits |

  posts Table:

  | Description                    | Image         | InfluencerUsername | InfluencerPost  | InfluencerPostCreatedAt | Status    |
  | ------------------------------ | ------------- | ------------------ | --------------- | ----------------------- | --------- |
  | Amazon Listing Optimization... | \[Drive Link] | jon-derkits        | \[LinkedIn URL] | 2025-06-18 19:30:01     | In review |

  Status field options:

  In review
  Approved
  Posted
  Rejected

  Example LinkedIn Visuals

  | Caption                                                                                            | Image                                                                                               |
  | -------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------- |
  | Boost Your Amazon ROI!  Data-driven strategies are your secret weapon...                    | View Image |
  | Boost Sales with 5–10% Higher Conversions!  Utilize Enhanced Brand Content and Smart PPC... | View Image |
  | Grow Your Amazon Business  WeSpark – Amazon Consulting Agency...                            | View Image |
  | Amazon Growth Strategies  Embrace the power of smart optimization...                     | View Image |

  ✅ Best For:

  Amazon agencies and FBA consultants
  B2B SaaS tools targeting eCommerce
  LinkedIn ghostwriters and creators
  Content marketing teams for Amazon sellers

nodes_used:
  ["@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.airtable","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.airtable","n8n-nodes-base.if","n8n-nodes-base.googleDrive","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.limit","n8n-nodes-base.googleDrive","n8n-nodes-base.linkedIn","n8n-nodes-base.airtable","n8n-nodes-base.scheduleTrigger"]

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metadata_json:
  {"categories":["automation","integration","content generation","social media"],"complexity":"medium","use_cases":["Automatically generate LinkedIn posts for Amazon sellers by repurposing influencer content","Hands-off scheduled LinkedIn publishing for marketing teams","Content ideation and branded image creation for ecommerce agencies","Ghostwriting LinkedIn posts at scale for clients"],"estimated_setup_minutes":20,"required_services":["Airtable","Apify (LinkedIn scraper)","OpenAI (GPT-4o)","Google Drive (OAuth2)","LinkedIn account (company or personal)","n8n"],"key_features":["Scrapes latest influencer LinkedIn posts","Uses GPT-4o to rewrite content tailored to Amazon sellers","Generates branded LinkedIn images via AI and saves to Google Drive","Stores and tracks posts in Airtable with workflow statuses","Daily scheduled publisher that posts approved items to LinkedIn and updates status to Posted」「Duplicate detection/skip if post already stored」「Configurable influencer list in Airtable and single-post-per-day publishing policy"],"target_audience":["Amazon agencies and FBA consultants","B2B SaaS companies targeting eCommerce","LinkedIn ghostwriters and creators for Amazon niche clients」「Content marketing teams for Amazon sellers"]}


name: Real-time PulsePoint Emergency Alerts to iMessage with AI Summaries
author_name: David Harvey
author_username: xtgy
categories: []
created_at: 2025-07-01T20:58:26.415Z
updated_at: 2025-07-01T20:58:26.415Z
url: https://n8n.io/workflows/5533
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:34
description:
  🚨 Emergency Alerts Reporter to iMessage

  This n8n template fetches real-time emergency incident alerts from PulsePoint for a specific agency and delivers them directly to any phone number via iMessage using the Blooio API. It's designed to keep users informed with clear, AI-summarized reports of emergency activity near them—automatically and reliably.

  Use cases are powerful and immediate:  
  Get real-time fire/medical alerts for your neighborhood.  
  Use it for family, local safety groups, or even emergency response teams.  
  Convert technical dispatch data into readable updates with emojis and plain English.

  🧠 Good to Know

  You’ll need a PulsePoint agency ID (see instructions below).  
  iMessages are sent using Blooio’s API (which supports Apple’s iMessage and fallback RCS/SMS).  
  Messages are AI-enhanced using OpenAI's o4-mini model to summarize incident reports with context and urgency.  
  The workflow runs every hour, but this can be configured to match your needs.  
  Each report is sent only once, thanks to persistent tracking of seen incident IDs in workflow static memory.

  ⚙️ How it Works

  Trigger: A Schedule Trigger (every hour) or manual start kicks off the flow.  
  Get Alerts: A code node fetches the latest PulsePoint incidents for a specified agency and decrypts the data.  
  Filter New Incidents: We store previously seen incident IDs to prevent duplicate alerts.  
  Merge Incidents: All new incident details are merged into a single payload.  
  Condition Check: If there are no new incidents, nothing is sent.  
  AI Summary: The incident data is passed to an AI agent for summarization with human-friendly emojis and formatting.  
  Send Message: The final summary is sent via Blooio’s API to your phone using iMessage.

  📝 How to Use

  Get Your PulsePoint Agency ID:  
     Visit https://web.pulsepoint.org.  
     Find your agency by location or name.  
     Inspect the API call or browser network log to get the agencyid (e.g. 19100 from a URL like ?agencyid=19100).  

  Set Up Blooio for Messaging:  
     Sign up at https://blooio.com.  
     Go to your account and retrieve your Bearer API Key.  
     Pricing details available on their pricing page.  
     Add your key to the HTTP Request node as a Bearer Token.  

  OpenAI API:  
     Create or use an existing OpenAI account.  
     Use the o4-mini model for efficient, readable summaries.  
     Get your OpenAI API key from https://platform.openai.com/account/api-keys.  

  Add Your Phone Number:  
     Replace +1111112222 with your actual number (international format).  
     You can also modify the message content or prepend special tags/emojis.

  ✅ Requirements

  PulsePoint agency ID** – See usage instructions above  
  OpenAI API Key** – Get API Key  
  Blooio Account & Bearer Token** – Get Started  
  Phone number** for iMessage delivery  

  🔧 Customizing This Workflow

  Change the schedule** to get alerts more or less frequently  
  Add filters** to only get alerts for specific incident types (e.g. fires, traffic accidents)  
  Send to groups**: Expand to send alerts to multiple recipients or use Slack instead of iMessage  
  Use different AI prompts** to get detailed, humorous, or abbreviated alerts depending on your audience  

  With just a few credentials and a phone number, you’ll have real-time incident alerts with human-friendly summaries at your fingertips.

  🛠️ Stay informed. Stay safe.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.manualTrigger","n8n-nodes-base.scheduleTrigger","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.code","n8n-nodes-base.if","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 5804 characters]

metadata_json:
  {"categories":["automation","integration","notifications","public safety"],"complexity":"medium","use_cases":["Receive real-time neighborhood fire/medical alerts","Notify family or local safety groups of nearby incidents","Provide readable summaries for emergency response teams","Convert technical dispatch data into plain-English notifications","Send group or multi-channel alerts (iMessage with SMS/RCS fallback)"],"estimated_setup_minutes":25,"required_services":["PulsePoint (agency ID)","Blooio (iMessage/RCS/SMS API & Bearer token)","OpenAI (o4-mini API key)","n8n instance"],"key_features":["Fetches real-time PulsePoint incidents for a specified agency","Persistent deduplication of incident IDs (static workflow memory)","AI-generated human-friendly summaries (OpenAI o4-mini) with emojis/urgency context","Delivers messages via Blooio to iMessage with SMS/RCS fallback","Configurable schedule trigger and optional incident-type filters"],"target_audience":["individuals/homeowners concerned about local safety","families and neighborhood watch groups","community safety organizers and volunteers (EMS/Fire liaison) means to receive concise alerts) ) ) )"]}


name: eBay Finances Data Access for AI Agents with MCP Server Integration
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T21:05:51.006Z
updated_at: 2025-07-01T21:05:51.006Z
url: https://n8n.io/workflows/5534
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator?

  Join the community

  Complete MCP server exposing 7 eBay Finances API operations to AI agents.

  ⚡ Quick Setup

  Import this workflow into your n8n instance
  Credentials Add eBay Finances API credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the eBay Finances API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://apiz.ebay.com{basePath}
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (7 total)

  🔧 Payout

  • GET /payout: List seller payouts with filters
  • GET /payout/{payout_Id}: Get single payout details
  🔧 Payout_Summary

  • GET /payout_summary: Show payout totals by status
  🔧 Seller_Funds_Summary

  • GET /seller_funds_summary: View pending/unreleased funds
  🔧 Transaction

  • GET /transaction: Search monetary transactions
  🔧 Transaction_Summary

  • GET /transaction_summary: Get transaction totals + holds
  🔧 Transfer

  • GET /transfer/{transfer_Id}: Retrieve eBay reimbursement details

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native eBay Finances API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool"]

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metadata_json:
  {"categories":["integration","automation","AI integration","e-commerce"],"complexity":"medium","use_cases":["Expose eBay Finances API to AI agents","Retrieve seller payouts, transactions and transfers","Build AI tools that query eBay financial data","Rapidly prototype AI assistants that need payment/transaction data","Bridge n8n workflows with Claude/Cursor/custom AI via MCP"],"estimated_setup_minutes":20,"required_services":["eBay Finances API (developer credentials)","n8n instance with MCP trigger node (self‑hosted or cloud)","AI agent/client (e.g., Claude Desktop, Cursor, custom AI)","Publicly reachable webhook/SSE endpoint or tunneling if self‑hosted"],"key_features":["MCP-compatible server exposing 7 eBay Finances endpoints (payout, payout_summary, seller_funds_summary, transaction, transaction_summary, transfer)","Automatic parameter population via $fromAI() expressions","Native eBay API request/response passthrough","Built-in n8n HTTP error handling and logging","Supports path, query, body and header parameters for AI calls','Easy to extend or add custom logic"],"target_audience":["n8n users and integrators","Developers building AI agents/tools","E-commerce/marketplace operators needing finance data access for automation or analysis','Fintech analysts wanting programmatic access to seller financials"]}


name: Internet Archive Search API Integration for AI Agents (3 Operations)
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T21:17:27.764Z
updated_at: 2025-07-01T21:17:27.764Z
url: https://n8n.io/workflows/5535
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  Complete MCP server exposing 3 Search Services API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add Search Services credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the Search Services API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.archive.org
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (3 total)

  🔧 Search (3 endpoints)

  • GET /search/v1/fields: Fields that can be requested
  • GET /search/v1/organic: Return relevance-based results from search queries

  • GET /search/v1/scrape: Scrape search results from Internet Archive, allowing a scrolling cursor

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native Search Services API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool"]

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metadata_json:
  {"categories":["integration","AI","web API","automation"],"complexity":"medium","use_cases":["Expose Internet Archive Search API to AI agents via MCP","Provide relevance-based search and scraping endpoints with cursor pagination","Connect AI clients (Claude, Cursor, custom apps) to archival search","Rapid prototyping of AI-enabled search tools"],"estimated_setup_minutes":20,"required_services":["n8n instance (with MCP capability)","Internet Archive Search API (https://api.archive.org) and Search Services credentials","AI agent or MCP-compatible client (e.g., Claude Desktop, Cursor, custom AI app)"],"key_features":["MCP trigger exposing 3 Search Services endpoints (fields, organic, scrape)","HTTP Request nodes proxying to api.archive.org","AI-driven parameter population via $fromAI() expressions","Supports scrolling cursor pagination for scrapes","Built-in n8n HTTP request error handling and logging"],"target_audience":["developers","AI builders/engineers","automation/ops engineers integrating AI tools with web APIs"]}


name: Internet Archive Wayback Machine API for AI Assistants
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T21:18:56.407Z
updated_at: 2025-07-01T21:18:56.407Z
url: https://n8n.io/workflows/5536
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  Complete MCP server exposing 2 Wayback API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community
  Import this workflow into your n8n instance
  Credentials Add Wayback API credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the Wayback API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.archive.org
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (2 total)

  🔧 Wayback (2 endpoints)

  • GET /wayback/v1/available: GET /wayback/v1/available
  • POST /wayback/v1/available: POST /wayback/v1/available

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native Wayback API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool"]

workflow_json_compressed:
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metadata_json:
  {"categories":["integration","automation","ai-assistants","web-archiving"],"complexity":"simple","use_cases":["Allow AI agents to query Wayback Machine availability endpoints","Enable AI tools to fetch archived page metadata and timestamps","Embed Wayback API as a tool in custom AI apps or agent workflows","Research and content verification using archived snapshots","Rapid prototyping of AI integrations with external HTTP APIs"],"estimated_setup_minutes":20,"required_services":["n8n instance with public webhook access","Internet Archive Wayback Machine API (api.archive.org) credentials or access","MCP-compatible AI agent or client (e.g., Claude Desktop, Cursor) — optional for testing"],"key_features":["Exposes Wayback /wayback/v1/available via MCP-compatible endpoints (GET and POST)","MCP Trigger node serves as server endpoint for AI agents","HTTP Request nodes proxy calls to api.archive.org","AI expressions ($fromAI()) auto-populate parameters from agent requests","Returns native Wayback API responses directly to agents (no extra mapping needed)"],"target_audience":["developers building AI agent integrations","no-code/low-code automation builders","AI/ML engineers adding external tool access to agents"]}


name: IP Geolocation Lookup with BigDataCloud API for AI Agents
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T21:27:24.500Z
updated_at: 2025-07-01T21:27:24.500Z
url: https://n8n.io/workflows/5539
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  Complete MCP server exposing 2 IP Geolocation API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community
  Import this workflow into your n8n instance
  Credentials Add IP Geolocation API credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the IP Geolocation API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.bigdatacloud.net
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (2 total)

  🔧 Data (2 endpoints)

  • GET /data/ip-geolocation-full: IP Geolocation with Confidence Area and Hazard Report API
  • GET /data/ip-geolocation-with-confidence: IP Geolocation with Confidence Area API

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native IP Geolocation API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool"]

workflow_json_compressed:
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metadata_json:
  {"categories":["integration","api","data enrichment","automation","geolocation"],"complexity":"simple","use_cases":["Enrich IP addresses with geolocation data for AI agents","Personalization and geotargeting in apps","Security and fraud detection (location-based checks)","Analytics and reporting on user locations","Providing geolocation lookups to custom AI tools or assistants"],"estimated_setup_minutes":200,"required_services":["BigDataCloud IP Geolocation API (api.bigdatacloud.net) — API credentials required","n8n instance (to import and run the workflow / MCP trigger)","AI agent or platform to connect to the MCP endpoints (e.g., Claude, Cursor) — optional"],"key_features":["Exposes two MCP-compatible endpoints for IP geolocation","Direct HTTP request nodes calling BigDataCloud API","AI-friendly $fromAI() expressions for automatic parameter population","Native API responses returned to calling AI agents","Built-in n8n HTTP error handling and logging, easy extensibility"],"target_audience":["Developers","AI engineers and integrators","Data engineers / analysts needing IP enrichment data"]}


name: Query Bicycle Incident Data with BikeWise API through MCP Server
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T21:28:34.770Z
updated_at: 2025-07-01T21:28:34.770Z
url: https://n8n.io/workflows/5540
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:34
description:
  Complete MCP server exposing 4 BikeWise API v2 API operations to AI agents.

  ⚡ Quick Setup
  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add BikeWise API v2 credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the BikeWise API v2 API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://bikewise.org/api
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (4 total)

  🔧 V2 (4 endpoints)

  • GET /v2/incidents: Paginated incidents matching parameters
  • GET /v2/incidents/{id}: GET /v2/incidents/{id}
  • GET /v2/locations: Unpaginated geojson response
  • GET /v2/locations/markers: Unpaginated geojson response with simplestyled markers

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native BikeWise API v2 API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 4120 characters]

metadata_json:
  {"categories":["integration","API","automation","AI integration"],"complexity":"medium","use_cases":["Expose BikeWise API v2 to AI agents via MCP","Query bicycle incident data (paginated)","Retrieve geojson location data and markers","Integrate BikeWise data into custom AI apps or agent workflows"],"estimated_setup_minutes":20,"required_services":["BikeWise API v2 (https://bikewise.org/api)","n8n instance (self‑hosted or cloud)","AI agents or clients supporting MCP (e.g., Claude, Cursor)"],"key_features":["MCP server endpoint exposing 4 BikeWise API v2 operations","Automatic parameter population via $fromAI() expressions","Native n8n HTTP request handling with built‑in error management","Returns native BikeWise API responses (incidents, locations, markers)","Supports path/query parameters, headers, and auth credentials (BikeWise API v2)"],"target_audience":["Developers","AI integrators/engineers","Data analysts working with civic/bike incident data"]}


name: Track AI Agent token usage and estimate costs in Google Sheets
author_name: Solomon
author_username: solomon
categories: []
created_at: 2025-07-01T21:30:24.313Z
updated_at: 2025-07-01T21:30:24.313Z
url: https://n8n.io/workflows/5541
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:32
description:
  This n8n template demonstrates how to obtain token usage from AI Agents and places the data into a spreadsheet that calculates the estimated cost of the execution.

  Obtaining the token usage from AI Agents is tricky, because it doesn't provide all the data from tool calls. This workflow taps into the workflow execution metadata to extract token usage information.

  Works well with OpenAI, Google and Anthropic. Other LLM providers might need small tweaks.

  How it works
  The AI Agent executes and then calls a subworkflow to calculate the token usage.
  The data is stored in Google Sheets
  The spreadsheet has formulas to calculate the estimated cost of the execution.

  How to use
  The AI Agent is used as an example. Feel free to replace this with other agents you have.
  Call the subworkflow AFTER all the other branches have finished executing.

  Requirements
  LLM account (OpenAI, Gemini...) for API usage.
  Google Drive and Sheets credentials
  n8n API key of your instance

nodes_used:
  ["n8n-nodes-base.manualTrigger","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.executeWorkflow","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.toolThink","n8n-nodes-base.set","n8n-nodes-base.n8n","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.splitOut","n8n-nodes-base.summarize","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatAnthropic","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 6520 characters]

metadata_json:
  {"categories":["automation","cost monitoring","AI / LLM integration","reporting","spreadsheets"],"complexity":"medium","use_cases":["Track token usage of AI Agents across executions","Estimate and report LLM API costs","Audit and reconcile tool/agent call usage","Centralize usage data into Google Sheets for analysis","Trigger cost calculations after agent workflows finish"],"estimated_setup_minutes":30,"required_services":["LLM provider account (OpenAI, Anthropic, Google Gemini or other LLM API)","Google Drive & Google Sheets credentials","n8n instance with API key"],"key_features":["Extracts token usage from n8n workflow execution metadata","Stores usage records in Google Sheets","Spreadsheet contains formulas to calculate estimated costs","Subworkflow designed to run after all agent branches complete","Compatible with multiple LLM providers (may need tweaks)"],"target_audience":["n8n users / automation engineers","Developers integrating LLMs","ML engineers and SREs monitoring model usage and cost"]}


name: BIN Card Information Lookup API Connector for AI Agents with Balance Check
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T21:39:31.341Z
updated_at: 2025-07-01T21:39:31.341Z
url: https://n8n.io/workflows/5542
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  Complete MCP server exposing 2 BIN Lookup API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add BIN Lookup API credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the BIN Lookup API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.bintable.com/v1
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (2 total)

  🔧 Balance (1 endpoints)

  • GET /balance: Check Balance

  🔧 {Bin} (1 endpoints)

  • GET /{bin}: Lookup for bin

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native BIN Lookup API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool"]

workflow_json_compressed:
  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

metadata_json:
  {"categories":["integration","api","data lookup","ai-integration"],"complexity":"simple","use_cases":["Card BIN lookup (card brand, issuer, country)","Check account/card balance","Payment validation and pre-authorization checks","Fraud detection and risk scoring","Expose BIN lookup as a tool for AI agents / chatbots"],"estimated_setup_minutes":200,"required_services":["Bintable BIN Lookup API (https://api.bintable.com)","n8n instance (self-hosted or n8n.cloud)","AI agents or clients that support MCP (e.g., Claude, Cursor, custom AI apps)","Public webhook endpoint or accessible n8n webhook"],"key_features":["MCP server endpoint for AI agents","Two exposed endpoints: GET /balance and GET /{bin}","Automatic parameter population via $fromAI() expressions","Native n8n HTTP request nodes calling the BIN Lookup API","Built-in HTTP error handling and logging in n8n workflows (retry/response) `,`Easily extensible to add custom logic or extra endpoints"],"target_audience":["Developers building AI tools or integrations","Fintech engineers and payment teams","Fraud/risk analysts and security teams integrating BIN data workflows\",\"AI/ML engineers integrating external tools for agents"]}


name: Create Evergreen Content with GitHub Dynamic Images & URL Redirects
author_name: Eduard
author_username: eduard
categories: []
created_at: 2025-07-01T23:04:05.588Z
updated_at: 2025-07-01T23:04:05.588Z
url: https://n8n.io/workflows/5546
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  Transform static digital assets into dynamic, self-updating powerhouses that stay relevant for years to come!

  This workflow solves a common problem: once you publish forms, emails, or templates, their content becomes frozen in time. Users discovering them months later see outdated information, missed opportunities, and stale offers.

  Stop losing opportunities to stale content – make your digital assets work harder and stay fresher, automatically!

  Here's how it works:
  🔗 Stable embed links mean your original assets never need updating
  🔄 Dynamic URL redirects that automatically point to the latest pages
  🖼️ Auto-updating images that showcase fresh offers or content
  📅 Scheduled updates keep everything current without manual intervention

  Perfect for:
  Workflow sticky notes that become evergreen marketing billboards
  Registration forms with current promotions
  Email signatures with latest offers  
  Website banners that stay seasonally relevant
  Any digital asset you want to "future-proof"

  The magic: Set it up once, embed the stable URLs/images in your content, then forget about it. Years later, users will still see fresh, as current information automatically pulled from your workflow.

  Requirements: Free accounts with GitHub (image storage) and shorten.rest  (URL redirects).

  Both can be swapped for your preferred services.

  Follow me on LinkedIn for more tips on AI automation and n8n workflows!

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.editImage","n8n-nodes-base.github","n8n-nodes-base.stickyNote","n8n-nodes-base.formTrigger","n8n-nodes-base.github","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.github","n8n-nodes-base.github","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 5928 characters]

metadata_json:
  {"categories":["automation","content management","marketing","integration"],"complexity":"medium","use_cases":["Make embedded assets evergreen (forms, banners, signatures)","Auto-updating marketing images (seasonal offers, promotions)","Maintain stable embed URLs that redirect to latest pages","Keep registration or landing page links current without republishing","Turn static templates into self-updating assets"],"estimated_setup_minutes":30,"required_services":["GitHub (for image storage)","shorten.rest (for URL redirects)"],"key_features":["Stable embed links that never need changing in published content","Dynamic URL redirects pointing to latest target pages","Auto-updating images hosted on GitHub","Scheduled updates to refresh content automatically","Webhook/HTTP endpoints for manual or external triggers"],"target_audience":["marketers","content creators","web developers / frontend engineers embedding assets on sites/emails/forms or templates creators"]}


name: Connect Buffer Social Media Management API to AI Agents via MCP Protocol
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T23:39:54.843Z
updated_at: 2025-07-01T23:39:54.843Z
url: https://n8n.io/workflows/5547
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:33
description:
  Complete MCP server exposing 18 Bufferapp API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add Bufferapp credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the Bufferapp API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.bufferapp.com/1/
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (18 total)

  🔧 Info (1 endpoints)

  • GET /info/configuration{mediaTypeExtension}: Get Configuration

  🔧 Links (1 endpoints)

  • GET /links/shares{mediaTypeExtension}: Get Link Shares

  🔧 Profiles (7 endpoints)

  • POST /profiles/{id}/schedules/update{mediaTypeExtension}: Update Profile Schedules
  • GET /profiles/{id}/schedules{mediaTypeExtension}: Get Profile Schedules
  • GET /profiles/{id}/updates/pending{mediaTypeExtension}: Get Pending Updates
  • POST /profiles/{id}/updates/reorder{mediaTypeExtension}: Reorder Profile Updates
  • GET /profiles/{id}/updates/sent{mediaTypeExtension}: Get Sent Updates
  • POST /profiles/{id}/updates/shuffle{mediaTypeExtension}: Shuffle Profile Updates
  • GET /profiles/{id}{mediaTypeExtension}: Get Profile Details

  🔧 Profiles{Mediatypeextension} (1 endpoints)

  • GET /profiles{mediaTypeExtension}: List Profiles

  🔧 Updates (7 endpoints)

  • POST /updates/create{mediaTypeExtension}: Create Status Update
  • POST /updates/{id}/destroy{mediaTypeExtension}: Delete Status Update
  • GET /updates/{id}/interactions{mediaTypeExtension}: Get Update Interactions
  • POST /updates/{id}/move_to_top{mediaTypeExtension}: Move Update to Top
  • POST /updates/{id}/share{mediaTypeExtension}: Share Update Now
  • POST /updates/{id}/update{mediaTypeExtension}: Edit Status Update
  • GET /updates/{id}{mediaTypeExtension}: Get Update Details

  🔧 User{Mediatypeextension} (1 endpoints)

  • GET /user{mediaTypeExtension}: Get User Details

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native Bufferapp API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool"]

workflow_json_compressed:
  [encoded data, 6108 characters]

metadata_json:
  {"categories":["integration","automation","social media","AI-integration"],"complexity":"medium","use_cases":["Enable AI agents to read and manage Buffer posts and schedules","Automate social media publishing and updates via AI","Expose Buffer API as MCP-compatible tool for AI apps","Fetch profile, update, link and user data for analytics or moderation workflows"],"estimated_setup_minutes":293,"required_services":["Buffer (Bufferapp) API credentials","n8n instance (self-hosted or n8n cloud)","AI agent or app that supports the MCP protocol (e.g., Claude Desktop, Cursor, custom MCP clients)"],"key_features":["MCP trigger exposing 18 Buffer API endpoints","Automatic parameter population via $fromAI() expressions","Direct passthrough of native Buffer API responses","Built-in HTTP error handling and logging via n8n","Operations include creating/updating/deleting/sharing updates, managing profiles/schedules, links, and user info"],"target_audience":["Developers building AI-agent integrations","AI/ML engineers wanting tool-access to social accounts","Social media managers who want automated AI workflows to control Buffer accounts (with developer help)"]}


name: SMS Messaging API Integration for AI Agents with BulkSMS
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T23:46:43.610Z
updated_at: 2025-07-01T23:46:43.610Z
url: https://n8n.io/workflows/5548
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  Complete MCP server exposing 15 BulkSMS JSON REST API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add BulkSMS JSON REST API credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the BulkSMS JSON REST API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.bulksms.com/v1
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (15 total)

  🔧 Blocked-Numbers (2 endpoints)

  • GET /blocked-numbers: Block Phone Number
  • POST /blocked-numbers: Create a blocked number

  🔧 Credit (1 endpoints)

  • POST /credit/transfer: Transfer Account Credits

  🔧 Messages (5 endpoints)

  • GET /messages: List Related Messages
  • POST /messages: Send Messages
  • GET /messages/send: Send message by simple GET or POST
  • GET /messages/{id}: Show Message
  • GET /messages/{id}/relatedReceivedMessages: List Related Messages

  🔧 Profile (1 endpoints)

  • GET /profile: Retrieve User Profile

  🔧 Rmm (1 endpoints)

  • POST /rmm/pre-sign-attachment: Generate Attachment Upload URL

  🔧 Webhooks (5 endpoints)

  • GET /webhooks: Update Webhook Settings
  • POST /webhooks: Create a webhook
  • DELETE /webhooks/{id}: Delete a webhook
  • GET /webhooks/{id}: Read a webhook
  • POST /webhooks/{id}: Update a webhook

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native BulkSMS JSON REST API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool"]

workflow_json_compressed:
  [encoded data, 7404 characters]

metadata_json:
  {"categories":["integration","communication","automation","ai-agent-integration","webhook-management"],"complexity":"medium","use_cases":["Allow AI agents to send SMS messages via BulkSMS","Expose BulkSMS API operations as MCP endpoints for AI tools","Manage webhooks and blocked numbers programmatically","Transfer account credits and retrieve account/profile data","Generate pre-signed upload URLs for attachments"],"estimated_setup_minutes":200,"required_services":["BulkSMS account / BulkSMS JSON REST API","n8n instance with MCP trigger support","Publicly reachable webhook URL (or tunnel like ngrok) for agent callbacks","AI agent or platform that can call MCP endpoints (e.g., Claude Desktop, Cursor, custom AI apps)"],"key_features":["Exposes 15 BulkSMS JSON REST API operations via MCP","MCP Trigger acts as server endpoint for AI agents","Automatic parameter population using $fromAI() expressions","Native n8n HTTP Request nodes with built-in error handling","Full passthrough of BulkSMS JSON responses to AI agents (no mapping required)」「Webhook create/read/update/delete and blocked-number management」「Attachment pre-sign URL generation for uploads"],"target_audience":["developers","AI engineers","automation engineers / integrators"]}


name: Access CO2 Measurement Data with CarbonDoomsDay API for AI Agents
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T23:49:13.065Z
updated_at: 2025-07-01T23:49:13.065Z
url: https://n8n.io/workflows/5550
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  Complete MCP server exposing 2 CarbonDoomsDay API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add CarbonDoomsDay credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the CarbonDoomsDay API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.carbondoomsday.com/api
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (2 total)

  🔧 Co2 (2 endpoints)

  • GET /co2/: Get CO2 Measurement by Date
  • GET /co2/{date}/: CO2 measurements from the Mauna Loa observatory.

  This data is made available through the good work of the people at the
  Mauna Loa observatory. Their release notes say:

      These data are made freely available to the public and the scientific
      community in the belief that their wide dissemination will lead to greater
      understanding and new scientific insights.

  We currently scrape the following sources:

    [co2_mlo_weekly.csv]
    [co2_mlo_surface-insitu_1_ccgg_DailyData.txt]
    [weekly_mlo.csv]

  We have daily CO2 measurements as far back as 1958.

  Learn about using pagination via [the 3rd party documentation].

  [co2_mlo_weekly.csv]: https://www.esrl.noaa.gov/gmd/webdata/ccgg/trends/co2_mlo_weekly.csv
  [co2_mlo_surface-insitu_1_ccgg_DailyData.txt]: ftp://aftp.cmdl.noaa.gov/data/trace_gases/co2/in-situ/surface/mlo/co2_mlo_surface-insitu_1_ccgg_DailyData.txt
  [weekly_mlo.csv]: http://scrippsco2.ucsd.edu/sites/default/files/data/in_situ_co2/weekly_mlo.csv
  [the 3rd party documentation]: http://www.django-rest-framework.org/api-guide/pagination/#pagenumberpagination

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native CarbonDoomsDay API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool"]

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metadata_json:
  {"categories":["integration","automation","data access","AI agent tooling","environmental data"],"complexity":"simple","use_cases":["Expose CO2 measurement endpoints to AI agents","Fetch historical Mauna Loa CO2 data for analysis or dashboards","Integrate CO2 data into chatbots or AI workflows","Rapid prototyping of AI tools that need environmental data","Education and research access to CO2 time series"],"estimated_setup_minutes":200,"required_services":["n8n (self-hosted or cloud)","CarbonDoomsDay API (https://api.carbondoomsday.com)","Mauna Loa / NOAA / Scripps data sources (used by the API)","MCP-compatible AI agents or clients (e.g., Claude Desktop, Cursor, custom agents)"],"key_features":["MCP trigger exposes API endpoints to AI agents","Two GET endpoints for CO2 measurements (general and date-specific)","Automatic parameter population via $fromAI() expressions","Native n8n HTTP request handling with built-in error management","Returns native CarbonDoomsDay API responses (full data structures)"],"target_audience":["Developers building AI agents and integrations","Data scientists and analysts needing CO2 time series","Climate researchers and educators needing quick API access to Mauna Loa data」「Integrators and automation engineers looking to expose APIs to AI tools"]}


name: AI Personal Assistant with GPT-4o: Email, Calendar, Search & CRM Integration
author_name: Jordan Lee
author_username: jordanlee
categories: []
created_at: 2025-07-01T23:56:27.537Z
updated_at: 2025-07-01T23:56:27.537Z
url: https://n8n.io/workflows/5552
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:32
description:
  This n8n template demonstrates how to use AI as a comprehensive personal assistant with multiple specialized agents.

  Use cases include email management, scheduling, web search, calculations, and more - all automated through AI coordination.

  Good to know
  This template integrates multiple AI services through OpenRouter

  Each agent specializes in different tasks (Gmail, Calendar, Search, etc.)

  Memory persistence maintains context across interactions

  How it works

  The workflow is triggered by Telegram messages (can be replaced with other triggers)

  A router node directs requests to the appropriate specialized agent

  Agents include:

  Gmail for email management

  Calculator for math operations

  Google Search for information retrieval

  Calendar for scheduling

  Contacts for CRM functions

  The OpenRouter Chat Model coordinates responses

  Final responses are sent back through Telegram

  How to use
  Connect your Telegram bot credentials

  Configure each service with appropriate API keys

  The system will automatically route requests to the right agent

  Requirements

  OpenRouter account for AI services

  Telegram bot token

  Google API credentials for relevant services

  Customising this workflow
  Add more specialized agents as needed

  Replace Telegram with other communication channels

  Adjust routing logic for different use cases

nodes_used:
  ["@n8n/n8n-nodes-langchain.toolCalculator","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolSerpApi","n8n-nodes-base.telegram","n8n-nodes-base.telegramTrigger","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.lmChatOpenRouter","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.perplexityTool"]

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metadata_json:
  {"categories":["automation","integration","ai","productivity","communication"],"complexity":"complex","use_cases":["Email management and drafting","Scheduling and calendar management","Web research and information retrieval","CRM/contact lookups and updates","On-the-fly calculations and utilities via chat interface"],"estimated_setup_minutes":60,"required_services":["OpenRouter (GPT-4o) account / API key","Telegram bot token","Google APIs (Gmail, Calendar, Contacts/People) and credentials","SerpApi key (Google Search integration)","Perplexity API key"],"key_features":["Multi-agent router that directs requests to specialized agents (Gmail, Calendar, Search, Contacts, Calculator)","OpenRouter chat model to coordinate agent responses","Memory persistence (buffer window) to maintain context across interactions","Telegram trigger and response channel (replaceable with other channels)","Integrated web search using SerpApi and Perplexity for information retrieval and fact-checking"],"target_audience":["Developers building conversational automations","Productivity-focused professionals and executive assistants","Small business owners and sales reps needing CRM/email automation workflows','Automation engineers and IT admins"]}


name: Automate ASMR Video Creation and Distribution with FalAI and Social Media Integration
author_name: Harrison P
author_username: purhar
categories: []
created_at: 2025-07-02T00:02:39.571Z
updated_at: 2025-07-02T00:02:39.571Z
url: https://n8n.io/workflows/5553
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:32
description:
  🎬 Create Unlimited Viral ASMR Videos — Fully Automated!
  Tired of spending hours brainstorming viral content, writing prompts, and juggling video uploads?

  This powerful n8n workflow automates your entire short-form ASMR video creation process, turning you into a content powerhouse — without burnout or repetitive manual work.

  ✅ No coding required
  ✅ Connects with cutting-edge AI video generation (FalAI)
  ✅ Publishes videos to TikTok, Instagram, YouTube, and more
  ✅ Tracks everything neatly in Google Sheets
  ✅ Ready for customization!

  ✨ How It Works
  Here’s how this workflow becomes your virtual content assistant:

  Brainstorm New Viral ASMR Objects
  Generates a creative, unused idea like:

  Glass Kiwi
  Crystal Banana
  Frozen Watermelon
  Ensures fresh content by checking your Google Sheet for duplicates.

  Generate a Detailed Video Prompt
  Transforms your object idea into a sensory-rich ASMR video script:


  “Imagine a crystal-clear kiwi sliced open, glistening under neon lights…”
  This prompt is crafted to produce hyper-realistic AI visuals perfect for short-form platforms.

  Create the ASMR Video with FalAI
  Sends the prompt to FalAI, an advanced video-generation API.

  Supports vertical aspect ratio for social reels.

  Automatically waits for video processing to complete.

  Save Video Details to Google Sheets
  Adds each new object and video URL to a Google Sheet.

  Maintains a running archive of all your generated videos.

  No more manual tracking or losing track of which videos you’ve already created!

  Upload to Blotato and Publish Across Platforms
  Uploads the video to Blotato’s media hosting service.

  Optionally posts the video automatically to:

  TikTok

  Instagram

  YouTube

  And easily extendable to Facebook, Pinterest, and more!

  Posts your captions, hashtags, and links seamlessly.

  🎯 Why You’ll Love This Workflow
  ✅ Saves hours of creative work and publishing time
  ✅ Eliminates manual uploads and spreadsheets
  ✅ Keeps your content fresh and consistent
  ✅ Highly customizable for new platforms or personal style
  ✅ Includes clear instructions and sticky notes for easy setup

  💬 Need Help?
  If you get stuck:

  Check the included sticky notes in the workflow canvas

  Or search for “N8N Assistant by Nskha” on ChatGPT, and upload your JSON file for personalized support.

  🎉 Supercharge Your Content Creation
  This isn’t just a workflow — it’s a full-scale ASMR content machine ready to scale your viral video production effortlessly. Automate your creativity and free up your time to focus on what matters most!

  ✅ Ready to impress your audience and grow your brand?
  Import this workflow today and start creating viral ASMR magic!

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metadata_json:
  {"categories":["automation","content-creation","social-media","ai-video-generation","integration"],"complexity":"complex","use_cases":["Automated ASMR short-form video generation","Batch-producing and publishing social reels (TikTok, Instagram, YouTube)","Maintaining a content inventory and preventing duplicate ideas","Generating sensory-rich video prompts for AI video generation","Hosting media and cross-posting with automated captions/hashtags"],"estimated_setup_minutes":60,"required_services":["FalAI (AI video generation API)","Blotato (media hosting)","TikTok API","Instagram Graph API / Creator API","YouTube Data API","Google Sheets API","OpenAI or compatible LM API (lmChatOpenAi)"],"key_features":["Automated brainstorming of unique ASMR objects","Duplicate-check against Google Sheets","Transforms ideas into detailed sensory ASMR video prompts","Creates vertical videos via FalAI and polls for processing completion","Saves video metadata and URLs to Google Sheets archive"],"target_audience":["Content creators and ASMR artists","Social media managers and marketers","Small agencies and solopreneurs focused on short-form video growth"]}


name: File Hash Verification for AI Agents with hashlookup CIRCL API
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-02T00:05:28.154Z
updated_at: 2025-07-02T00:05:28.154Z
url: https://n8n.io/workflows/5554
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  Complete MCP server exposing 11 hashlookup CIRCL API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community
  Import this workflow into your n8n instance
  Credentials Add hashlookup CIRCL API credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the hashlookup CIRCL API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://hashlookup.circl.lu
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (11 total)

  🔧 Bulk (2 endpoints)

  • POST /bulk/md5: Bulk Search MD5 Hashes
  • POST /bulk/sha1: Bulk Search SHA1 Hashes

  🔧 Children (1 endpoints)

  • GET /children/{sha1}/{count}/{cursor}: Return children from a given SHA1.  A number of element to return and an offset must be given. If not set it will be the 100 first elements. A cursor must be given to paginate over. The starting cursor is 0.

  🔧 Info (1 endpoints)

  • GET /info: Get Database Info

  🔧 Lookup (3 endpoints)

  • GET /lookup/md5/{md5}: Lookup MD5.
  • GET /lookup/sha1/{sha1}: Lookup SHA-1.
  • GET /lookup/sha256/{sha256}: Lookup SHA-256.

  🔧 Parents (1 endpoints)

  • GET /parents/{sha1}/{count}/{cursor}: Return parents from a given SHA1. A number of element to return and an offset must be given. If not set it will be the 100 first elements. A cursor must be given to paginate over. The starting cursor is 0.

  🔧 Session (2 endpoints)

  • GET /session/create/{name}: Create a session key to keep search context. The session is attached to a name. After the session is created, the header hashlookup_session can be set to the session name.
  • GET /session/get/{name}: Return set of matching and non-matching hashes from a session.

  🔧 Stats (1 endpoints)

  • GET /stats/top: Get Top Queries

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native hashlookup CIRCL API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

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name: Business Lead Generation with Apify Web Scraping and Google Sheets Storage
author_name: Jordan Lee
author_username: jordanlee
categories: []
created_at: 2025-07-02T00:06:32.693Z
updated_at: 2025-07-02T00:06:32.693Z
url: https://n8n.io/workflows/5555
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:33
description:
  This flexible template scrapes business listings for any industry and location, perfect for sales teams, marketers, and researchers.

  Good to know
  Works with any business category (restaurants, contractors, retailers, etc.)
  Fully customizable search parameters
  Results automatically organized in Google Sheets
  Built-in delay ensures scraping completes before data collection

  How it works

  Trigger: Manual or scheduled start
  Apify Configuration: Sets scraping parameters (industry, location, data fields)
  Scraping Execution: Runs the web scraping job
  Data Processing: Cleans and structures the raw data
  Storage: Saves results to your Google Sheets

  What is Apify?
  Apify is a webscraping tool, in this workflow the data is scraped from a google maps scraper:

  https://apify.com/compass/crawler-google-places

  How to use

  Apify Small # Lead Generation (Purple)

  https://apify.com/compass/crawler-google-places


  Add location and industry to scrape (Apify)
  Add the number of leads to output (Apify)
  Copy over the JSON file into N8N
  Copy & paste API endpoint "Get Run URL" in N8N

  Apify Large # Lead Generation (Grey)

  Configure the Manual Trigger

  When clicking 'Execute workflow' node is ready to use as-is
  This triggers the entire lead generation process

  Setup "Start Results (Apify)" Node
  Get Your Apify API Information

  Go to Apify.com and create a free account
  Navigate to Settings → Integrations → API tokens
  Copy your API token
  Find the Google Maps scraper actor ID:

  Configure the HTTP Request (start results)

  Method: POST
  URL: Replace "enter apify (get run)" with:
  https://api.apify.com/v2/acts/nwua9Gu5YrADL7ZDj/runs?token=YOUR_API_TOKEN


  C. Customize the JSON Body Parameters
  In the JSON body, modify these key fields:
  Location & Search:

  "locationQuery": Change "Toronto" to your target city
  "searchStringsArray": Change ["barber"] to your business type

  Examples: ["restaurants"], ["dentists"], ["contractors"]

  Configure the HTTP Request (start results)
  Method : Get
  Url: enter the get dataset URL from Apify

  Split Out Node
  Select fields to append in the google sheet

  Test the Configuration

  Click Execute workflow to test
  Check that the Apify job starts successfully
  Note the job ID returned for the next section

  This section initiates the scraping process and should complete in 30-60 seconds depending on your lead count.

  Setup Google Sheets

  Create a new Google Sheet with these columns:

  title (business name)
  address (full address)
  state (state/province)
  neighborhood (area/district)
  phone (contact number)
  emails (email addresses)


  Copy your Google Sheets document ID for workflow configuration

  Requirements
  Apify account
  Google Sheets document
  Google OAuth credentials

  Customization Options
  For different use cases:

  Lead Gen: Get business leads
  Local SEO: Collect competitor data
  Market Research: Analyze industry trends

  Advanced mofications:
  Add email enrichment
  Integrate with CRM systems
  Set up automatic daily runs

nodes_used:
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metadata_json:
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name: Audio & Video Data Search and Analysis with Clarify API and AI Agent Integration
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-02T00:13:52.614Z
updated_at: 2025-07-02T00:13:52.614Z
url: https://n8n.io/workflows/5557
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  Complete MCP server exposing 21 api.clarify.io API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add api.clarify.io credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the api.clarify.io API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.clarify.io/
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (21 total)

  🔧 V1 (21 endpoints)

  • GET /v1/bundles: Add Media to Track
  • POST /v1/bundles: Create a bundle
  • DELETE /v1/bundles/{bundle_id}: Delete a bundle
  • GET /v1/bundles/{bundle_id}: Get a bundle
  • PUT /v1/bundles/{bundle_id}: Update a bundle
  • GET /v1/bundles/{bundle_id}/insights: Get bundle insights
  • POST /v1/bundles/{bundle_id}/insights: Request an insight to be run
  • GET /v1/bundles/{bundle_id}/insights/{insight_id}: Get bundle insight
  • DELETE /v1/bundles/{bundle_id}/metadata: Delete bundle metadata
  • GET /v1/bundles/{bundle_id}/metadata: Get bundle metadata
  • PUT /v1/bundles/{bundle_id}/metadata: Update bundle metadata
  • DELETE /v1/bundles/{bundle_id}/tracks: Delete bundle tracks
  • GET /v1/bundles/{bundle_id}/tracks: Get bundle tracks
  • POST /v1/bundles/{bundle_id}/tracks: Add a track for a bundle
  • PUT /v1/bundles/{bundle_id}/tracks: Update a tracks for a bundle
  • DELETE /v1/bundles/{bundle_id}/tracks/{track_id}: Delete a bundle track
  • GET /v1/bundles/{bundle_id}/tracks/{track_id}: Get bundle track
  • PUT /v1/bundles/{bundle_id}/tracks/{track_id}: Add media to a track
  • GET /v1/reports/scores: Generate Group Report
  • GET /v1/reports/trends: Generate Trends Report
  • GET /v1/search: Search Bundles

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native api.clarify.io API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

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metadata_json:
  {"categories":["integration","automation","data processing","AI","media"],"complexity":"medium","use_cases":["Expose Clarify API as an MCP tool for AI agents","Search and analyze audio/video bundles","Generate group and trends reports from media data","Automate ingestion and management of media tracks and metadata","Integrate AI agents (Claude, Cursor, custom apps) with Clarify operations"],"estimated_setup_minutes":20,"required_services":["Clarify API (api.clarify.io) - API key/credentials","n8n instance (hosted or self-hosted)","AI agent or client that supports MCP/webhook tool integration (e.g., Claude Desktop, Cursor, custom AI apps)"],"key_features":["MCP trigger exposing 21 Clarify API endpoints (v1 bundles, tracks, insights, reports, search)","Automatic parameter population via $fromAI() expressions","Native n8n HTTP Request nodes returning Clarify responses","Built-in HTTP error handling and logging","Zero/low configuration required to connect AI agents via MCP webhook URL (SSE support where applicable)"],"target_audience":["developers","AI engineers","data analysts / media analysts"]}


