Todo for AI MCP Server

中文版本 | English

A Model Context Protocol (MCP) server that provides AI assistants with access to the Todo for AI task management system. This allows AI assistants to retrieve tasks, get project information, create new tasks, and submit task feedback through a standardized interface.

🚀 Try it now: Visit https://todo4ai.org/ to experience our product!

Features

• 🔍 Get Project Tasks: Retrieve pending tasks for a specific project with status filtering

• 📋 Get Task Details: Fetch detailed information about individual tasks with project context

• ➕ Create Tasks: Create new tasks with full metadata support

• ✅ Submit Feedback: Update task status and provide completion feedback

• 📊 Project Information: Get comprehensive project statistics and recent tasks

• 🔄 Automatic Retry: Built-in retry mechanism for network failures

• 📝 Comprehensive Logging: Detailed logging with configurable levels

• ⚙️ Flexible Configuration: Environment variables and config file support

• 🛡️ Type Safety: Full TypeScript support with strict type checking

• 🚀 Performance: Optimized build with incremental compilation

• 🌐 HTTP Transport: Modern HTTP-based communication using Streamable HTTP protocol

• 🔒 Security: DNS rebinding protection, CORS support, and origin validation

• 📡 Real-time: Server-Sent Events (SSE) support for real-time communication

• 🔄 Session Management: Automatic session handling with timeout and cleanup

Related MCP server: Todoist MCP Server

Installation

From npm (Recommended)

npm install -g @todo-for-ai/mcp

From Source

git clone <repository-url>
cd todo-mcp
npm install
npm run build
npm link

Configuration

Transport Type

The MCP server uses HTTP Transport: Modern HTTP-based communication with Server-Sent Events (SSE) support for real-time communication.

Environment Variables

Create a .env file or set environment variables:

# Required: API authentication token
TODO_API_TOKEN=your-api-token

# Optional: Todo API base URL (default: https://todo4ai.org/todo-for-ai/api/v1)
TODO_API_BASE_URL=http://localhost:50110/todo-for-ai/api/v1

# Optional: API timeout in milliseconds (default: 10000)
TODO_API_TIMEOUT=10000

# HTTP Transport Configuration
# Optional: HTTP server port (default: 3000)
TODO_HTTP_PORT=3000

# Optional: HTTP server host (default: 127.0.0.1)
TODO_HTTP_HOST=127.0.0.1

# Optional: Session timeout in milliseconds (default: 300000 = 5 minutes)
TODO_SESSION_TIMEOUT=300000

# Optional: Enable DNS rebinding protection (default: true)
TODO_DNS_PROTECTION=true

# Optional: Allowed origins for CORS (comma-separated, default: http://localhost:*,https://localhost:*)
TODO_ALLOWED_ORIGINS=http://localhost:*,https://localhost:*

# Optional: Maximum concurrent connections (default: 100)
TODO_MAX_CONNECTIONS=100

# Optional: Log level (default: info)
LOG_LEVEL=info

# Optional: Environment (default: development)
NODE_ENV=development

Configuration File

Alternatively, create a config.json file:

{
  "apiBaseUrl": "https://todo4ai.org/todo-for-ai/api/v1",
  "apiTimeout": 10000,
  "apiToken": "your-api-token",
  "logLevel": "info"
}

Usage

Command Line

HTTP Transport

# Start with HTTP transport on default port 3000
todo-for-ai-mcp --api-token your-token

# HTTP transport with custom port and host
todo-for-ai-mcp --api-token your-token --http-port 8080 --http-host 0.0.0.0

# HTTP transport with session timeout and security options
todo-for-ai-mcp --api-token your-token \
  --session-timeout 600000 \
  --dns-protection \
  --allowed-origins "http://localhost:*,https://localhost:*"

# Using environment variables for HTTP transport
TODO_API_TOKEN=your-token \
TODO_HTTP_PORT=3000 \
TODO_HTTP_HOST=127.0.0.1 \
todo-for-ai-mcp

# With environment variables
TODO_API_BASE_URL=http://your-server:8080 TODO_API_TOKEN=your-token todo-for-ai-mcp

# With command line arguments
todo-for-ai-mcp --api-base-url http://your-server:8080 --api-token your-token --log-level debug

# Mixed configuration (CLI args take priority over environment variables)
TODO_API_BASE_URL=http://localhost:50110 todo-for-ai-mcp --api-token your-token --log-level info

Configuration Options

The MCP server supports configuration through both command line arguments and environment variables, with the following priority order:

Priority: Command Line Arguments > Environment Variables > Defaults

| HTTP Port | --http-port | TODO_HTTP_PORT | 3000 | | HTTP Host | --http-host | TODO_HTTP_HOST | 127.0.0.1 | | Session Timeout | --session-timeout | TODO_SESSION_TIMEOUT | 300000 (ms) | | DNS Protection | --dns-protection | TODO_DNS_PROTECTION | true (for http) | | Allowed Origins | --allowed-origins | TODO_ALLOWED_ORIGINS | http://localhost:*,https://localhost:* | | Max Connections | --max-connections | TODO_MAX_CONNECTIONS | 100 |

Additional Options:

Examples:

# Show help information
todo-for-ai-mcp --help
todo-for-ai-mcp -h

# Show version information
todo-for-ai-mcp --version
todo-for-ai-mcp -v

# Using command line arguments (API token is required)
todo-for-ai-mcp --api-token your-token --log-level debug

# Using environment variables
export TODO_API_TOKEN="your-token"
export LOG_LEVEL="info"
todo-for-ai-mcp

# Using custom API base URL
todo-for-ai-mcp --api-base-url http://localhost:50110/todo-for-ai/api/v1 --api-token your-token

# Mixed approach (CLI args override env vars)
TODO_API_TOKEN=your-token todo-for-ai-mcp --log-level debug

HTTP Transport Usage

When using HTTP transport, the MCP server runs as a standalone HTTP server that can be accessed via REST API and Server-Sent Events (SSE).

Starting HTTP Server

# Start HTTP server on default port 3000
todo-for-ai-mcp --api-token your-token --transport http

# The server will be available at:
# - Health check: http://127.0.0.1:3000/health
# - MCP endpoint: http://127.0.0.1:3000/mcp

HTTP Endpoints

• GET /health: Health check endpoint

• POST /mcp: Client-to-server communication (JSON-RPC)

• GET /mcp: Server-to-client notifications (SSE)

• DELETE /mcp: Session termination

Session Management

HTTP transport uses session-based communication:

1. Initialize: Send an initialize request to create a new session

2. Session ID: Server returns a session ID in the Mcp-Session-Id header

3. Subsequent requests: Include the session ID in all future requests

4. Cleanup: Sessions automatically expire after the configured timeout

Example HTTP Client Usage

// Initialize session
const initResponse = await fetch('http://127.0.0.1:3000/mcp', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({
    jsonrpc: '2.0',
    id: 1,
    method: 'initialize',
    params: {
      protocolVersion: '2024-11-05',
      capabilities: { tools: {} },
      clientInfo: { name: 'my-client', version: '1.0.0' }
    }
  })
});

const sessionId = initResponse.headers.get('Mcp-Session-Id');

// Use session for subsequent requests
const toolsResponse = await fetch('http://127.0.0.1:3000/mcp', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'Mcp-Session-Id': sessionId
  },
  body: JSON.stringify({
    jsonrpc: '2.0',
    id: 2,
    method: 'tools/list',
    params: {}
  })
});

IDE Integration

Claude Desktop

Note: Claude Desktop currently supports Stdio transport. For HTTP transport support, you'll need to start the server separately and use a custom MCP client that supports HTTP transport.

Traditional Stdio configuration (if supported):

{
  "mcpServers": {
    "todo-for-ai": {
      "command": "npx",
      "args": [
        "-y", "@todo-for-ai/mcp@latest",
        "--api-token", "your-api-token-here"
      ]
    }
  }
}

Alternative with environment variables:

{
  "mcpServers": {
    "todo-for-ai": {
      "command": "npx",
      "args": ["-y", "@todo-for-ai/mcp@latest"],
      "env": {
        "TODO_API_TOKEN": "your-api-token-here"
      }
    }
  }
}

For local development (custom API base URL):

{
  "mcpServers": {
    "todo-for-ai": {
      "command": "npx",
      "args": [
        "-y", "@todo-for-ai/mcp@latest",
        "--api-base-url", "http://localhost:50110/todo-for-ai/api/v1",
        "--api-token", "your-api-token-here"
      ]
    }
  }
}

HTTP transport setup:

1. Start the HTTP server separately:

# Terminal 1: Start the MCP server in HTTP mode
TODO_API_TOKEN=your-token todo-for-ai-mcp --http-port 3000

2. The server will be available at http://127.0.0.1:3000/mcp for custom MCP clients that support HTTP transport.

Cursor IDE

Add to your Cursor configuration:

{
  "mcpServers": {
    "todo-for-ai": {
      "command": "npx",
      "args": [
        "-y", "@todo-for-ai/mcp@latest",
        "--api-token", "your-api-token-here"
      ]
    }
  }
}

Local Development

For development with local Todo for AI server:

{
  "mcpServers": {
    "todo-for-ai-local": {
      "command": "node",
      "args": ["/path/to/todo-mcp/dist/index.js"],
      "env": {
        "TODO_API_BASE_URL": "http://localhost:50110",
        "LOG_LEVEL": "debug"
      }
    }
  }
}

Available Tools

1. get_project_tasks_by_name

Get all pending tasks for a project by name.

Parameters:

• project_name (string, required): Name of the project

• status_filter (array, optional): Filter by task status (default: ["todo", "in_progress", "review"])

Example:

{
  "project_name": "My Project",
  "status_filter": ["todo", "in_progress"]
}

2. get_task_by_id

Get detailed information about a specific task.

Parameters:

• task_id (integer, required): ID of the task to retrieve

Example:

{
  "task_id": 123
}

3. create_task

Create a new task in the specified project.

Parameters:

• project_id (integer, required): ID of the project

• title (string, required): Task title

• content (string, optional): Task content/description

• status (string, optional): Initial status (default: "todo")

• priority (string, optional): Task priority (default: "medium")

• due_date (string, optional): Due date in YYYY-MM-DD format

• assignee (string, optional): Person assigned to the task

• tags (array, optional): Tags associated with the task

• is_ai_task (boolean, optional): Whether this is an AI task (default: true)

• ai_identifier (string, optional): AI identifier (default: "MCP Client")

Example:

{
  "project_id": 10,
  "title": "Implement new feature",
  "content": "Add user authentication to the application",
  "status": "todo",
  "priority": "high",
  "due_date": "2024-12-31",
  "tags": ["authentication", "security"]
}

4. submit_task_feedback

Submit feedback and update status for a task.

Parameters:

• task_id (integer, required): ID of the task

• project_name (string, required): Name of the project

• feedback_content (string, required): Feedback description

• status (string, required): New status ("in_progress", "review", "done", "cancelled")

• ai_identifier (string, optional): AI identifier (default: "MCP Client")

Example:

{
  "task_id": 123,
  "project_name": "My Project",
  "feedback_content": "Completed the implementation as requested",
  "status": "done",
  "ai_identifier": "Claude"
}

5. get_project_info

Get detailed project information including statistics and recent tasks.

Parameters:

• project_id (integer, optional): ID of the project to retrieve

• project_name (string, optional): Name of the project to retrieve

Note: Either project_id or project_name must be provided.

Example:

{
  "project_name": "My Project"
}

6. list_agents

List Agent identities available to the current user.

Agent collaboration tools return a short operational summary first, followed by a JSON: block with the complete API response. Use the summary for the next action and the JSON block for exact IDs, states, lease timestamps, match scores, and event payloads.

Parameters:

• status (string, optional): Filter by Agent status (active, paused, offline, disabled)

• search (string, optional): Search Agent name or description

• page (integer, optional): Page number (default: 1)

• per_page (integer, optional): Page size (default: 20)

Example:

{
  "status": "active",
  "per_page": 20
}

7. create_agent

Create an Agent identity and declare its collaboration capabilities.

Parameters:

• name (string, required): Agent display name

• description (string, optional): Agent purpose or operating notes

• kind (string, optional): Agent kind (assistant, autonomous, coordinator, external; default: assistant)

• status (string, optional): Initial Agent status (active, paused, offline, disabled; default: active)

• provider (string, optional): Provider name

• model (string, optional): Model or runtime identifier

• capabilities (array of strings, optional): Capability keywords used for automatic task matching

• config (object, optional): Agent configuration metadata

Example:

{
  "name": "Frontend Builder",
  "kind": "autonomous",
  "provider": "openai",
  "model": "gpt-5-codex",
  "capabilities": ["frontend", "react", "typescript", "ui"]
}

8. update_agent

Update an Agent identity, status, model metadata, or capabilities.

Parameters:

• agent_id (integer, required): ID of the Agent

• name (string, optional): Agent display name

• description (string, optional): Agent purpose or operating notes

• kind (string, optional): Agent kind (assistant, autonomous, coordinator, external)

• status (string, optional): Agent status (active, paused, offline, disabled)

• provider (string, optional): Provider name

• model (string, optional): Model or runtime identifier

• capabilities (array of strings, optional): Capability keywords used for automatic task matching

• config (object, optional): Agent configuration metadata

Example:

{
  "agent_id": 1,
  "status": "active",
  "capabilities": ["frontend", "react", "typescript", "review"]
}

9. heartbeat_agent

Record an Agent heartbeat and optionally update its availability status.

Parameters:

• agent_id (integer, required): ID of the Agent

• status (string, optional): New Agent status (active, paused, offline, disabled)

Example:

{
  "agent_id": 1,
  "status": "active"
}

10. list_review_queue

List Agent assignments that need human feedback or final review.

For human_feedback items, resume the assignment with update_task_assignment using state: "running", task_status: "in_progress", and feedback_content so the worker Agent receives the human response. For final_review items, approve with state: "done" and task_status: "done", or send changes back with state: "running" plus feedback_content.

Parameters:

• action (string, optional): Filter queue by all, human_feedback, or final_review (default: all)

• page (integer, optional): Page number (default: 1)

• per_page (integer, optional): Page size (default: 20)

Example:

{
  "action": "final_review",
  "per_page": 20
}

11. list_agent_assignments

List task assignments for an Agent.

Parameters:

• agent_id (integer, required): ID of the Agent

• state (string, optional): Assignment state filter

• page (integer, optional): Page number (default: 1)

• per_page (integer, optional): Page size (default: 20)

Example:

{
  "agent_id": 1,
  "state": "running"
}

12. list_task_assignments

List Agent assignments for a task. Use state: "active" to see current non-terminal assignments with live leases.

Parameters:

• task_id (integer, required): ID of the task

• state (string, optional): Assignment state filter, or active

• page (integer, optional): Page number (default: 1)

• per_page (integer, optional): Page size (default: 20)

Example:

{
  "task_id": 42,
  "state": "active"
}

13. list_task_events

List collaboration events for a task so Agents can inspect handoffs, claims, review requests, assignment updates, and lease expirations.

Parameters:

• task_id (integer, required): ID of the task

• page (integer, optional): Page number (default: 1)

• per_page (integer, optional): Page size (default: 20)

Example:

{
  "task_id": 42,
  "per_page": 20
}

14. claim_agent_task

Claim a specific task, or the next claimable task, for an Agent. Claiming creates an assignment, starts an Agent run, and records a task collaboration event.

Parameters:

• agent_id (integer, required): ID of the Agent

• task_id (integer, optional): Specific task ID to claim

• project_id (integer, optional): Project filter when claiming the next available task

• lease_seconds (integer, optional): Lease duration in seconds (default: 1800)

• match_capabilities (boolean, optional): Prefer tasks whose tags or text match the Agent capabilities when claiming automatically (default: true)

• dispatch_source (string, optional): Set to human when manually dispatching a specific task to the Agent

• dispatch_notes (string, optional): Notes stored in run_metadata.dispatch_notes for a manual dispatch

• run_metadata (object, optional): Runtime metadata

Example:

{
  "agent_id": 1,
  "project_id": 10,
  "lease_seconds": 1800,
  "match_capabilities": true
}

Manual dispatch example:

{
  "agent_id": 1,
  "task_id": 42,
  "lease_seconds": 1800,
  "dispatch_source": "human",
  "dispatch_notes": "Focus on the API contract and update tests before marking review."
}

15. update_agent_assignment

Update an Agent assignment state, progress, feedback, lease, or execution result. Marking an assignment as done moves the task to review so a human can approve the final completion.

Parameters:

• agent_id (integer, required): ID of the Agent

• assignment_id (integer, required): ID of the assignment

• state (string, optional): New assignment state

• progress_rate (integer, optional): Progress percent from 0 to 100

• notes (string, optional): Internal assignment notes

• feedback_content (string, optional): Human-readable task feedback

• output_summary (string, optional): Execution output summary

• error (string, optional): Execution error details

• lease_seconds (integer, optional): Extend lease by this duration in seconds

• task_status (string, optional): Optional task status override

• run_metadata (object, optional): Runtime metadata

Example:

{
  "agent_id": 1,
  "assignment_id": 42,
  "state": "done",
  "progress_rate": 100,
  "feedback_content": "Implementation completed and ready for review."
}

16. update_task_assignment

Update a task assignment as the current user or coordinator. Use this with items from list_review_queue to approve final review, resume work, cancel an assignment, or add human feedback without acting as the worker Agent.

Parameters:

• task_id (integer, required): ID of the task

• assignment_id (integer, required): ID of the assignment

• state (string, optional): New assignment state

• progress_rate (integer, optional): Progress percent from 0 to 100

• notes (string, optional): Internal assignment notes

• feedback_content (string, optional): Human-readable task feedback

• output_summary (string, optional): Execution output summary

• error (string, optional): Execution error details

• lease_seconds (integer, optional): Extend lease by this duration in seconds

• task_status (string, optional): Optional task status override

• run_metadata (object, optional): Runtime metadata

Examples:

{
  "task_id": 42,
  "assignment_id": 7,
  "state": "done",
  "progress_rate": 100,
  "task_status": "done"
}

{
  "task_id": 42,
  "assignment_id": 7,
  "state": "running",
  "task_status": "in_progress",
  "feedback_content": "Please address the review comments and continue."
}

Development

Prerequisites

• Node.js 18+

• npm or yarn

• Todo for AI backend server running

Setup

# Clone and install
git clone <repository-url>
cd todo-mcp
npm install

# Copy environment file
cp .env.example .env
# Edit .env with your configuration

# Development mode
npm run dev

# Build
npm run build

# Test
npm test

# Lint
npm run lint

Project Structure

todo-mcp/
├── src/
│   ├── index.ts          # Main entry point
│   ├── server.ts         # MCP server implementation
│   ├── api-client.ts     # Todo API client
│   ├── config.ts         # Configuration management
│   ├── logger.ts         # Logging utilities
│   ├── error-handler.ts  # Error handling
│   └── types.ts          # TypeScript types
├── dist/                 # Compiled JavaScript
├── package.json
├── tsconfig.json
├── .env.example
└── README.md

Troubleshooting

Common Issues

1. Connection Failed

   • Ensure Todo for AI backend is running

   • Check TODO_API_BASE_URL is correct

   • Verify network connectivity

2. Authentication Errors

   • Check if API token is required

   • Verify TODO_API_TOKEN is set correctly

3. Tool Not Found

   • Ensure MCP server is properly registered in IDE

   • Check IDE configuration syntax

   • Restart IDE after configuration changes

Debug Mode

Enable debug logging:

LOG_LEVEL=debug todo-for-ai-mcp

Health Check

Test connection to Todo API:

curl http://localhost:50110/api/health

License

MIT License - see LICENSE file for details.

Contributing

1. Fork the repository

2. Create a feature branch

3. Make your changes

4. Add tests

5. Submit a pull request

Support

For issues and questions:

• Create an issue on GitHub

• Check the troubleshooting section

• Review the logs with debug mode enabled

🌟 Ready to supercharge your AI workflow? Visit https://todo4ai.org/ and experience the power of AI-driven task management!