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docs: Update documentation for new AI/config architecture and finalize cleanup

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This commit updates all relevant documentation (READMEs, docs/*, .cursor/rules) to accurately reflect the finalized unified AI service architecture and the new configuration system (.taskmasterconfig + .env/mcp.json). It also includes the final code cleanup steps related to the refactoring.

Key Changes:

1.  **Documentation Updates:**

    *   Revised `README.md`, `README-task-master.md`, `assets/scripts_README.md`, `docs/configuration.md`, and `docs/tutorial.md` to explain the new configuration split (.taskmasterconfig vs .env/mcp.json).

    *   Updated MCP configuration examples in READMEs and tutorials to only include API keys in the `env` block.

    *   Added/updated examples for using the `--research` flag in `docs/command-reference.md`, `docs/examples.md`, and `docs/tutorial.md`.

    *   Updated `.cursor/rules/ai_services.mdc`, `.cursor/rules/architecture.mdc`, `.cursor/rules/dev_workflow.mdc`, `.cursor/rules/mcp.mdc`, `.cursor/rules/taskmaster.mdc`, `.cursor/rules/utilities.mdc`, and `.cursor/rules/new_features.mdc` to align with the new architecture, removing references to old patterns/files.

    *   Removed internal rule links from user-facing rules (`taskmaster.mdc`, `dev_workflow.mdc`, `self_improve.mdc`).

    *   Deleted outdated example file `docs/ai-client-utils-example.md`.

2.  **Final Code Refactor & Cleanup:**

    *   Corrected `update-task-by-id.js` by removing the last import from the old `ai-services.js`.

    *   Refactored `update-subtask-by-id.js` to correctly use the unified service and logger patterns.

    *   Removed the obsolete export block from `mcp-server/src/core/task-master-core.js`.

    *   Corrected logger implementation in `update-tasks.js` for CLI context.

    *   Updated API key mapping in `config-manager.js` and `ai-services-unified.js`.

3.  **Configuration Files:**

    *   Updated API keys in `.cursor/mcp.json`, replacing `GROK_API_KEY` with `XAI_API_KEY`.

    *   Updated `.env.example` with current API key names.

    *   Added `azureOpenaiBaseUrl` to `.taskmasterconfig` example.

4.  **Task Management:**

    *   Marked documentation subtask 61.10 as 'done'.

    *   Includes various other task content/status updates from the diff summary.

5.  **Changeset:**

    *   Added `.changeset/cuddly-zebras-matter.md` for user-facing `expand`/`expand-all` improvements.

This commit concludes the major architectural refactoring (Task 61) and ensures the documentation accurately reflects the current system.
This commit is contained in:
Eyal Toledano
2025-04-25 14:43:12 -04:00
parent 7c8d464b82
commit 9b5c625bd0
24 changed files with 477 additions and 925 deletions
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257
docs/ai-client-utils-example.md
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@@ -1,257 +0,0 @@
# AI Client Utilities for MCP Tools
This document provides examples of how to use the new AI client utilities with AsyncOperationManager in MCP tools.
## Basic Usage with Direct Functions
```javascript
// In your direct function implementation:
import {
getAnthropicClientForMCP,
getModelConfig,
handleClaudeError
} from '../utils/ai-client-utils.js';
export async function someAiOperationDirect(args, log, context) {
try {
// Initialize Anthropic client with session from context
const client = getAnthropicClientForMCP(context.session, log);
// Get model configuration with defaults or session overrides
const modelConfig = getModelConfig(context.session);
// Make API call with proper error handling
try {
const response = await client.messages.create({
model: modelConfig.model,
max_tokens: modelConfig.maxTokens,
temperature: modelConfig.temperature,
messages: [{ role: 'user', content: 'Your prompt here' }]
});
return {
success: true,
data: response
};
} catch (apiError) {
// Use helper to get user-friendly error message
const friendlyMessage = handleClaudeError(apiError);
return {
success: false,
error: {
code: 'AI_API_ERROR',
message: friendlyMessage
}
};
}
} catch (error) {
// Handle client initialization errors
return {
success: false,
error: {
code: 'AI_CLIENT_ERROR',
message: error.message
}
};
}
}
```
## Integration with AsyncOperationManager
```javascript
// In your MCP tool implementation:
import {
AsyncOperationManager,
StatusCodes
} from '../../utils/async-operation-manager.js';
import { someAiOperationDirect } from '../../core/direct-functions/some-ai-operation.js';
export async function someAiOperation(args, context) {
const { session, mcpLog } = context;
const log = mcpLog || console;
try {
// Create operation description
const operationDescription = `AI operation: ${args.someParam}`;
// Start async operation
const operation = AsyncOperationManager.createOperation(
operationDescription,
async (reportProgress) => {
try {
// Initial progress report
reportProgress({
progress: 0,
status: 'Starting AI operation...'
});
// Call direct function with session and progress reporting
const result = await someAiOperationDirect(args, log, {
reportProgress,
mcpLog: log,
session
});
// Final progress update
reportProgress({
progress: 100,
status: result.success ? 'Operation completed' : 'Operation failed',
result: result.data,
error: result.error
});
return result;
} catch (error) {
// Handle errors in the operation
reportProgress({
progress: 100,
status: 'Operation failed',
error: {
message: error.message,
code: error.code || 'OPERATION_FAILED'
}
});
throw error;
}
}
);
// Return immediate response with operation ID
return {
status: StatusCodes.ACCEPTED,
body: {
success: true,
message: 'Operation started',
operationId: operation.id
}
};
} catch (error) {
// Handle errors in the MCP tool
log.error(`Error in someAiOperation: ${error.message}`);
return {
status: StatusCodes.INTERNAL_SERVER_ERROR,
body: {
success: false,
error: {
code: 'OPERATION_FAILED',
message: error.message
}
}
};
}
}
```
## Using Research Capabilities with Perplexity
```javascript
// In your direct function:
import {
getPerplexityClientForMCP,
getBestAvailableAIModel
} from '../utils/ai-client-utils.js';
export async function researchOperationDirect(args, log, context) {
try {
// Get the best AI model for this operation based on needs
const { type, client } = await getBestAvailableAIModel(
context.session,
{ requiresResearch: true },
log
);
// Report which model we're using
if (context.reportProgress) {
await context.reportProgress({
progress: 10,
status: `Using ${type} model for research...`
});
}
// Make API call based on the model type
if (type === 'perplexity') {
// Call Perplexity
const response = await client.chat.completions.create({
model: context.session?.env?.PERPLEXITY_MODEL || 'sonar-medium-online',
messages: [{ role: 'user', content: args.researchQuery }],
temperature: 0.1
});
return {
success: true,
data: response.choices[0].message.content
};
} else {
// Call Claude as fallback
// (Implementation depends on specific needs)
// ...
}
} catch (error) {
// Handle errors
return {
success: false,
error: {
code: 'RESEARCH_ERROR',
message: error.message
}
};
}
}
```
## Model Configuration Override Example
```javascript
// In your direct function:
import { getModelConfig } from '../utils/ai-client-utils.js';
// Using custom defaults for a specific operation
const operationDefaults = {
model: 'claude-3-haiku-20240307', // Faster, smaller model
maxTokens: 1000, // Lower token limit
temperature: 0.2 // Lower temperature for more deterministic output
};
// Get model config with operation-specific defaults
const modelConfig = getModelConfig(context.session, operationDefaults);
// Now use modelConfig in your API calls
const response = await client.messages.create({
model: modelConfig.model,
max_tokens: modelConfig.maxTokens,
temperature: modelConfig.temperature
// Other parameters...
});
```
## Best Practices
1. **Error Handling**:
- Always use try/catch blocks around both client initialization and API calls
- Use `handleClaudeError` to provide user-friendly error messages
- Return standardized error objects with code and message
2. **Progress Reporting**:
- Report progress at key points (starting, processing, completing)
- Include meaningful status messages
- Include error details in progress reports when failures occur
3. **Session Handling**:
- Always pass the session from the context to the AI client getters
- Use `getModelConfig` to respect user settings from session
4. **Model Selection**:
- Use `getBestAvailableAIModel` when you need to select between different models
- Set `requiresResearch: true` when you need Perplexity capabilities
5. **AsyncOperationManager Integration**:
- Create descriptive operation names
- Handle all errors within the operation function
- Return standardized results from direct functions
- Return immediate responses with operation IDs

12
docs/command-reference.md
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@@ -52,6 +52,9 @@ task-master show 1.2
```bash
# Update tasks from a specific ID and provide context
task-master update --from=<id> --prompt="<prompt>"
# Update tasks using research role
task-master update --from=<id> --prompt="<prompt>" --research
```
## Update a Specific Task
@@ -60,7 +63,7 @@ task-master update --from=<id> --prompt="<prompt>"
# Update a single task by ID with new information
task-master update-task --id=<id> --prompt="<prompt>"
# Use research-backed updates with Perplexity AI
# Use research-backed updates
task-master update-task --id=<id> --prompt="<prompt>" --research
```
@@ -73,7 +76,7 @@ task-master update-subtask --id=<parentId.subtaskId> --prompt="<prompt>"
# Example: Add details about API rate limiting to subtask 2 of task 5
task-master update-subtask --id=5.2 --prompt="Add rate limiting of 100 requests per minute"
# Use research-backed updates with Perplexity AI
# Use research-backed updates
task-master update-subtask --id=<parentId.subtaskId> --prompt="<prompt>" --research
```
@@ -187,9 +190,12 @@ task-master fix-dependencies
## Add a New Task
```bash
# Add a new task using AI
# Add a new task using AI (main role)
task-master add-task --prompt="Description of the new task"
# Add a new task using AI (research role)
task-master add-task --prompt="Description of the new task" --research
# Add a task with dependencies
task-master add-task --prompt="Description" --dependencies=1,2,3

108
docs/configuration.md
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@@ -1,53 +1,89 @@
# Configuration
Task Master can be configured through environment variables in a `.env` file at the root of your project.
Taskmaster uses two primary methods for configuration:
## Required Configuration
1. **`.taskmasterconfig` File (Project Root - Recommended for most settings)**
- `ANTHROPIC_API_KEY`: Your Anthropic API key for Claude (Example: `ANTHROPIC_API_KEY=sk-ant-api03-...`)
- This JSON file stores most configuration settings, including AI model selections, parameters, logging levels, and project defaults.
- **Location:** Create this file in the root directory of your project.
- **Management:** Use the `task-master models --setup` command (or `models` MCP tool) to interactively create and manage this file. Manual editing is possible but not recommended unless you understand the structure.
- **Example Structure:**
```json
{
"models": {
"main": {
"provider": "anthropic",
"modelId": "claude-3-7-sonnet-20250219",
"maxTokens": 64000,
"temperature": 0.2
},
"research": {
"provider": "perplexity",
"modelId": "sonar-pro",
"maxTokens": 8700,
"temperature": 0.1
},
"fallback": {
"provider": "anthropic",
"modelId": "claude-3-5-sonnet",
"maxTokens": 64000,
"temperature": 0.2
}
},
"global": {
"logLevel": "info",
"debug": false,
"defaultSubtasks": 5,
"defaultPriority": "medium",
"projectName": "Your Project Name",
"ollamaBaseUrl": "http://localhost:11434/api",
"azureOpenaiBaseUrl": "https://your-endpoint.openai.azure.com/"
}
}
```
## Optional Configuration
2. **Environment Variables (`.env` file or MCP `env` block - For API Keys Only)**
- Used **exclusively** for sensitive API keys and specific endpoint URLs.
- **Location:**
- For CLI usage: Create a `.env` file in your project root.
- For MCP/Cursor usage: Configure keys in the `env` section of your `.cursor/mcp.json` file.
- **Required API Keys (Depending on configured providers):**
- `ANTHROPIC_API_KEY`: Your Anthropic API key.
- `PERPLEXITY_API_KEY`: Your Perplexity API key.
- `OPENAI_API_KEY`: Your OpenAI API key.
- `GOOGLE_API_KEY`: Your Google API key.
- `MISTRAL_API_KEY`: Your Mistral API key.
- `AZURE_OPENAI_API_KEY`: Your Azure OpenAI API key (also requires `AZURE_OPENAI_ENDPOINT`).
- `OPENROUTER_API_KEY`: Your OpenRouter API key.
- `XAI_API_KEY`: Your X-AI API key.
- **Optional Endpoint Overrides (in .taskmasterconfig):**
- `AZURE_OPENAI_ENDPOINT`: Required if using Azure OpenAI key.
- `OLLAMA_BASE_URL`: Override the default Ollama API URL (Default: `http://localhost:11434/api`).
- `MODEL` (Default: `"claude-3-7-sonnet-20250219"`): Claude model to use (Example: `MODEL=claude-3-opus-20240229`)
- `MAX_TOKENS` (Default: `"4000"`): Maximum tokens for responses (Example: `MAX_TOKENS=8000`)
- `TEMPERATURE` (Default: `"0.7"`): Temperature for model responses (Example: `TEMPERATURE=0.5`)
- `DEBUG` (Default: `"false"`): Enable debug logging (Example: `DEBUG=true`)
- `LOG_LEVEL` (Default: `"info"`): Console output level (Example: `LOG_LEVEL=debug`)
- `DEFAULT_SUBTASKS` (Default: `"3"`): Default subtask count (Example: `DEFAULT_SUBTASKS=5`)
- `DEFAULT_PRIORITY` (Default: `"medium"`): Default priority (Example: `DEFAULT_PRIORITY=high`)
- `PROJECT_NAME` (Default: `"MCP SaaS MVP"`): Project name in metadata (Example: `PROJECT_NAME=My Awesome Project`)
- `PROJECT_VERSION` (Default: `"1.0.0"`): Version in metadata (Example: `PROJECT_VERSION=2.1.0`)
- `PERPLEXITY_API_KEY`: For research-backed features (Example: `PERPLEXITY_API_KEY=pplx-...`)
- `PERPLEXITY_MODEL` (Default: `"sonar-medium-online"`): Perplexity model (Example: `PERPLEXITY_MODEL=sonar-large-online`)
**Important:** Settings like model ID selections (`main`, `research`, `fallback`), `maxTokens`, `temperature`, `logLevel`, `defaultSubtasks`, `defaultPriority`, and `projectName` are **managed in `.taskmasterconfig`**, not environment variables.
## Example .env File
## Example `.env` File (for API Keys)
```
# Required
ANTHROPIC_API_KEY=sk-ant-api03-your-api-key
# Required API keys for providers configured in .taskmasterconfig
ANTHROPIC_API_KEY=sk-ant-api03-your-key-here
PERPLEXITY_API_KEY=pplx-your-key-here
# OPENAI_API_KEY=sk-your-key-here
# GOOGLE_API_KEY=AIzaSy...
# etc.
# Optional - Claude Configuration
MODEL=claude-3-7-sonnet-20250219
MAX_TOKENS=4000
TEMPERATURE=0.7
# Optional - Perplexity API for Research
PERPLEXITY_API_KEY=pplx-your-api-key
PERPLEXITY_MODEL=sonar-medium-online
# Optional - Project Info
PROJECT_NAME=My Project
PROJECT_VERSION=1.0.0
# Optional - Application Configuration
DEFAULT_SUBTASKS=3
DEFAULT_PRIORITY=medium
DEBUG=false
LOG_LEVEL=info
# Optional Endpoint Overrides
# AZURE_OPENAI_ENDPOINT=https://your-azure-endpoint.openai.azure.com/
# OLLAMA_BASE_URL=http://custom-ollama-host:11434/api
```
## Troubleshooting
### Configuration Errors
- If Task Master reports errors about missing configuration or cannot find `.taskmasterconfig`, run `task-master models --setup` in your project root to create or repair the file.
- Ensure API keys are correctly placed in your `.env` file (for CLI) or `.cursor/mcp.json` (for MCP) and are valid.
### If `task-master init` doesn't respond:
Try running it with Node directly:

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docs/examples.md
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@@ -51,3 +51,33 @@ Can you analyze the complexity of our tasks to help me understand which ones nee
```
Can you show me the complexity report in a more readable format?
```
### Breaking Down Complex Tasks
```
Task 5 seems complex. Can you break it down into subtasks?
```
(Agent runs: `task-master expand --id=5`)
```
Please break down task 5 using research-backed generation.
```
(Agent runs: `task-master expand --id=5 --research`)
### Updating Tasks with Research
```
We need to update task 15 based on the latest React Query v5 changes. Can you research this and update the task?
```
(Agent runs: `task-master update-task --id=15 --prompt="Update based on React Query v5 changes" --research`)
### Adding Tasks with Research
```
Please add a new task to implement user profile image uploads using Cloudinary, research the best approach.
```
(Agent runs: `task-master add-task --prompt="Implement user profile image uploads using Cloudinary" --research`)

30
docs/tutorial.md
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@@ -27,21 +27,22 @@ npm i -g task-master-ai
"env": {
"ANTHROPIC_API_KEY": "YOUR_ANTHROPIC_API_KEY_HERE",
"PERPLEXITY_API_KEY": "YOUR_PERPLEXITY_API_KEY_HERE",
"MODEL": "claude-3-7-sonnet-20250219",
"PERPLEXITY_MODEL": "sonar-pro",
"MAX_TOKENS": 64000,
"TEMPERATURE": 0.2,
"DEFAULT_SUBTASKS": 5,
"DEFAULT_PRIORITY": "medium"
"OPENAI_API_KEY": "YOUR_OPENAI_KEY_HERE",
"GOOGLE_API_KEY": "YOUR_GOOGLE_KEY_HERE",
"MISTRAL_API_KEY": "YOUR_MISTRAL_KEY_HERE",
"OPENROUTER_API_KEY": "YOUR_OPENROUTER_KEY_HERE",
"XAI_API_KEY": "YOUR_XAI_KEY_HERE",
"AZURE_OPENAI_API_KEY": "YOUR_AZURE_KEY_HERE",
"OLLAMA_API_KEY": "YOUR_OLLAMA_KEY_HERE"
}
}
}
}
```
2. **Enable the MCP** in your editor settings
3. **Enable the MCP** in your editor settings
3. **Prompt the AI** to initialize Task Master:
4. **Prompt the AI** to initialize Task Master:
```
Can you please initialize taskmaster-ai into my project?
@@ -53,9 +54,9 @@ The AI will:
- Set up initial configuration files
- Guide you through the rest of the process
4. Place your PRD document in the `scripts/` directory (e.g., `scripts/prd.txt`)
5. Place your PRD document in the `scripts/` directory (e.g., `scripts/prd.txt`)
5. **Use natural language commands** to interact with Task Master:
6. **Use natural language commands** to interact with Task Master:
```
Can you parse my PRD at scripts/prd.txt?
@@ -247,13 +248,16 @@ If during implementation, you discover that:
Tell the agent:
```
We've changed our approach. We're now using Express instead of Fastify. Please update all future tasks to reflect this change.
We've decided to use MongoDB instead of PostgreSQL. Can you update all future tasks (from ID 4) to reflect this change?
```
The agent will execute:
```bash
task-master update --from=4 --prompt="Now we are using Express instead of Fastify."
task-master update --from=4 --prompt="Now we are using MongoDB instead of PostgreSQL."
# OR, if research is needed to find best practices for MongoDB:
task-master update --from=4 --prompt="Update to use MongoDB, researching best practices" --research
```
This will rewrite or re-scope subsequent tasks in tasks.json while preserving completed work.
@@ -296,7 +300,7 @@ The agent will execute:
task-master expand --all
```
For research-backed subtask generation using Perplexity AI:
For research-backed subtask generation using the configured research model:
```
Please break down task 5 using research-backed generation.