MCP Server Implementation Guide

What is MCP?

MCP (Model Control Protocol) is a protocol that enables communication between Cursor IDE and AI language models like Claude. It allows you to create custom server implementations that can handle various AI-powered features in Cursor.

How It Works

The MCP server acts as a bridge between Cursor IDE and the AI model (like Claude). Here's the basic flow:

1. Cursor sends requests to the MCP server
2. The MCP server processes these requests and forwards them to the AI model
3. The AI model generates responses
4. The MCP server formats and sends these responses back to Cursor

Features

- Custom AI model integration - Request/Response handling - Websocket communication - Configuration management - Error handling - Rate limiting - Authentication

Setting Up Your Own MCP Server

Prerequisites

- Python 3.8+ - FastAPI - Anthropic API key (for Claude integration) - Cursor IDE

Installation

git clone https://github.com/dharakpatel/mcp_server.git
cd mcp_server
pip install -r requirements.txt

Configuration

Create a config.json file in your project root:

{
    "server": {
        "host": "localhost",
        "port": 8000,
        "debug": false
    },
    "anthropic": {
        "api_key": "your-api-key-here",
        "model": "claude-3-sonnet-20240229"
    },
    "cursor": {
        "allowed_origins": ["http://localhost:3000"],
        "max_tokens": 4096,
        "timeout": 30
    },
    "security": {
        "enable_auth": true,
        "auth_token": "your-secret-token",
        "rate_limit": {
            "requests_per_minute": 60
        }
    }
}

Integrating with Cursor

1. Open Cursor IDE 2. Go to Settings 3. Navigate to AI Settings 4. Set Custom MCP Server URL to http://localhost:8000 (or your server URL) 5. Add your authentication token if enabled

API Endpoints

Main Endpoints

- /v1/chat/completions - Main chat completion endpoint - /v1/health - Server health check - /v1/models - Available models information

WebSocket Endpoint

- /ws - WebSocket connection for real-time communication

Error Handling

The server implements comprehensive error handling: - Invalid requests - Authentication errors - Rate limiting - Timeout handling - Model errors

Best Practices

1. Always use environment variables for sensitive data 2. Implement proper logging 3. Use rate limiting to prevent abuse 4. Implement proper error handling 5. Keep the configuration file secure 6. Regular monitoring and maintenance

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License

Support

For issues and questions, please open an issue in the GitHub repository.