@todo-for-ai/mcp/README.md

Model Context Protocol server for Todo for AI task management system with Streamable HTTP transport. Provides AI assistants with access to task management, project information, and feedback submission capabilities through modern HTTP-based communication.

# Todo for AI MCP Server

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.

## 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

## Installation

### From npm (Recommended)

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

### From Source

```bash
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:

```bash
# 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:

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

## Usage

### Command Line

#### HTTP Transport

```bash
# 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**

| Configuration | CLI Argument | Environment Variable | Default |
|---------------|--------------|---------------------|---------|
| API Base URL | `--api-base-url`, `--base-url` | `TODO_API_BASE_URL` | `https://todo4ai.org/todo-for-ai/api/v1` |
| API Token | `--api-token`, `--token` | `TODO_API_TOKEN` | **Required** |
| API Timeout | `--api-timeout`, `--timeout` | `TODO_API_TIMEOUT` | `10000` (ms) |
| Log Level | `--log-level` | `LOG_LEVEL` | `info` |

| 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:**

| Option | CLI Argument | Description |
|--------|--------------|-------------|
| Help | `--help`, `-h` | Show help message and exit |
| Version | `--version`, `-v` | Show version information and exit |

**Examples:**

```bash
# 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

```bash
# 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

```javascript
// 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):**

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

**Alternative with environment variables:**

```json
{
  "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):**

```json
{
  "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:

```bash
# 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:

```json
{
  "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:

```json
{
  "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:**
```json
{
  "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:**
```json
{
  "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:**
```json
{
  "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:**
```json
{
  "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:**
```json
{
  "project_name": "My Project"
}
```

## Development

### Prerequisites

- Node.js 18+
- npm or yarn
- Todo for AI backend server running

### Setup

```bash
# 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:

```bash
LOG_LEVEL=debug todo-for-ai-mcp
```

### Health Check

Test connection to Todo API:

```bash
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