- Add @types/better-sqlite3 dependency
- Remove duplicate database mock file
- Fix property spread order in workflow builder to prevent overwrites
- All 75 tests now pass with no linting errors
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- Create comprehensive test directory structure
- Implement better-sqlite3 mock for Vitest
- Add node factory using fishery for test data generation
- Create workflow builder with fluent API
- Add infrastructure validation tests
- Update testing checklist to reflect progress
All Phase 2 tasks completed successfully with 7 tests passing.
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- Remove Jest and all related packages
- Install Vitest with coverage support
- Create vitest.config.ts with path aliases
- Set up global test configuration
- Migrate all 6 test files to Vitest syntax
- Update TypeScript configuration for better Vitest support
- Create separate tsconfig.build.json for clean builds
- Fix all import/module issues in tests
- All 68 tests passing successfully
- Current coverage baseline: 2.45%
Phase 1 of testing suite improvement complete.
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- Updated all Dockerfiles from node:20-alpine to node:22-alpine
- Addresses known vulnerabilities in older Alpine images
- Provides better long-term support with Node.js 22 LTS (until April 2027)
- Updated documentation to reflect new base image version
- Tested and verified compatibility with all dependencies
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- Updated n8n from 1.102.4 to 1.103.2
- Updated n8n-core from 1.101.2 to 1.102.1
- Updated n8n-workflow from 1.99.1 to 1.100.0
- Updated @n8n/n8n-nodes-langchain from 1.101.2 to 1.102.1
- Rebuilt node database with 532 nodes
- Bumped version to 2.7.21
- All validation tests passing
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- Added proper SIGTERM/SIGINT signal handlers to stdio-wrapper.ts
- Removed problematic trap commands from docker-entrypoint.sh
- Added STOPSIGNAL directive to Dockerfile for explicit signal handling
- Implemented graceful shutdown in MCP server with database cleanup
- Added stdin close detection for proper cleanup when Claude Desktop closes the pipe
- Containers now properly exit with the --rm flag, preventing accumulation
- Added --init flag to all Docker configuration examples
- Updated documentation with container lifecycle management best practices
- Bumped version to 2.7.20
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- Added support for n8n-nodes-langchain.* → nodes-langchain.* normalization
- Implemented case-insensitive node name matching (e.g., chattrigger → chatTrigger)
- Added smart camelCase detection for common patterns (trigger, request, sheets, etc.)
- Fixed get_node_documentation tool to use same normalization logic as other tools
- Updated all 7 node lookup locations to use normalized types for alternatives
- Enhanced getNodeTypeAlternatives() to normalize all generated alternatives
All MCP tools now consistently handle various format variations:
- nodes-langchain.chatTrigger (correct format)
- n8n-nodes-langchain.chatTrigger (package format)
- n8n-nodes-langchain.chattrigger (package + wrong case)
- nodes-langchain.chattrigger (wrong case only)
- @n8n/n8n-nodes-langchain.chatTrigger (full npm format)
Bump version to 2.7.19
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- Updated n8n from 1.101.1 to 1.102.4
- Updated n8n-core from 1.100.0 to 1.101.2
- Updated n8n-workflow from 1.98.0 to 1.99.1
- Updated @n8n/n8n-nodes-langchain from 1.100.1 to 1.101.2
- Rebuilt node database with 531 nodes
- All validation tests passing
- Updated README.md badges to reflect new versions
- Added reminder to update badges in MEMORY_N8N_UPDATE.md
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- Updated n8n from 1.101.1 to 1.102.4
- Updated n8n-core from 1.100.0 to 1.101.2
- Updated n8n-workflow from 1.98.0 to 1.99.1
- Updated @n8n/n8n-nodes-langchain from 1.100.1 to 1.101.2
- Rebuilt node database with 531 nodes
- All validation tests passing
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- Changed misleading 'total' field to 'returned' to clarify it's the count in current page
- Added 'hasMore' boolean flag for clear pagination indication
- Added '_note' guidance when more data is available
- Applied same improvements to n8n_list_executions for consistency
Performance improvements:
- Tool now returns only minimal metadata instead of full workflow structure
- Reduced response size by ~95% (from thousands to ~10 tokens per workflow)
- Eliminated token limit errors when listing workflows with many nodes
- Updated descriptions and documentation to clarify minimal response
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- Fixed health check to use correct /healthz endpoint instead of /health
- Added MCP version (mcpVersion) and supported n8n version (supportedN8nVersion) to health check response
- Added versionNote field with instructions for AI agents about manual version verification
- n8n API limitation: instance version cannot be determined automatically
- Updated axios usage for healthz endpoint access with proper error handling
- Added workflowNodeType field to all node-returning MCP tools
- AI agents now receive both internal format (nodes-base.webhook) and workflow format (n8n-nodes-base.webhook)
- Created getWorkflowNodeType() utility to construct proper n8n format from package name
- Solves issue where AI agents would search nodes and use wrong format in workflows
- No database changes required - uses existing package_name field
- Updated: search_nodes, get_node_info, get_node_essentials, get_node_as_tool_info, validate_node_operation
- Updated CHANGELOG.md with comprehensive documentation of the changes
This completes the fix for issue #71, ensuring AI agents can seamlessly create workflows
with the correct node type format without manual intervention.
- Add centralized normalizeNodeType utility to handle prefix conversion
- n8n-nodes-base.* → nodes-base.*
- @n8n/n8n-nodes-langchain.* → nodes-langchain.*
- Update all 9 affected MCP tools to use normalized node types
- AI agents can now use node types directly from n8n workflow exports
- Maintains backward compatibility with existing shortened prefixes
- Add comprehensive test coverage for all affected methods
Fixes#71🤖 Generated with [Claude Code](https://claude.ai/code)
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- Remove examples from get_node_essentials responses
- Remove examples from validate_node_operation when errors occur
- Update documentation to reflect removal of examples
- Keep helpful format hints in get_node_for_task (different purpose)
The auto-generated examples were misleading AI agents with incorrect
configurations (e.g., Slack "channel" vs "select" property). Tools
now focus on validation and error messages instead of examples.
🤖 Generated with [Claude Code](https://claude.ai/code)
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- Redesigned documentation to be utilitarian and AI-agent focused
- Removed all pleasantries, emojis, and conversational language
- Added concrete numbers throughout (528 nodes, 108 triggers, 264 AI tools)
- Updated all tool descriptions with practical, actionable information
- Enhanced examples with actual return structures and usage patterns
- Made Code node guides prominently featured in overview
- Verified documentation accuracy through extensive testing
- Standardized format across all 30+ tool documentation files
Documentation now optimized for token efficiency while maintaining
clarity and completeness for AI agent consumption.
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- Migrated all 40 MCP tools documentation to modular structure
- Created comprehensive documentation with both essentials and full details
- Organized tools by category: discovery, configuration, validation, templates, workflow_management, system, special
- Fixed all TODO placeholders with informative, precise content
- Each tool now has concise description, key tips, and full documentation
- Improved documentation quality: 30-40% more concise while maintaining usefulness
- Fixed TypeScript compilation issues and removed orphaned content
- All tools accessible via tools_documentation MCP endpoint
- Build successful with zero errors
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- Switch from package.json to package.runtime.json in runtime stage
- Reduces image size by 82% (from ~1.5GB to ~280MB)
- 10x faster builds (1-2 minutes vs 12 minutes)
- No functional changes - uses pre-built database from git
- Aligns Railway image with main Dockerfile optimization
This dramatically improves Railway deployment performance while
maintaining full functionality.
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Co-Authored-By: Claude <noreply@anthropic.com>
- Add Railway-specific Docker image build to CI/CD workflow
- Builds n8n-mcp-railway image alongside standard image
- Railway image optimized for AMD64 architecture
- Automatically published to ghcr.io on main branch pushes
- Create comprehensive Railway deployment documentation
- Step-by-step deployment guide with security best practices
- Claude Desktop connection instructions via mcp-remote
- Troubleshooting guide for common issues
- Architecture details and single-instance design explanation
- Update README with Railway documentation link
- Removed inline Railway content to keep README focused
- Added link to dedicated Railway deployment guide
This enables zero-configuration cloud deployment of n8n-mcp
with automatic HTTPS, global access, and built-in monitoring.
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