MCP Server

Project Overview

Built on FastAPI and MCP (Model Context Protocol), this project enables standardized context interaction between AI models and development environments. It enhances the scalability and maintainability of AI applications by simplifying model deployment, providing efficient API endpoints, and ensuring consistency in model input and output, making it easier for developers to integrate and manage AI tasks.

MCP (Model Context Protocol) is a unified protocol for context interaction between AI models and development environments. This project provides a Python-based MCP server implementation that supports basic MCP protocol features, including initialization, sampling, and session management.

Features

JSON-RPC 2.0: Request-response communication based on standard JSON-RPC 2.0 protocol
SSE Connection: Support for Server-Sent Events connections for real-time notifications
Modular Design: Modular architecture for easy extension and customization
Asynchronous Processing: High-performance service using FastAPI and asynchronous IO
Complete Client: Includes a full test client implementation

Project Structure

mcp_server/
├── mcp_server.py         # MCP server main program
├── mcp_client.py         # MCP client test program
├── routers/
│   ├── __init__.py       # Router package initialization
│   └── base_router.py    # Base router implementation
├── requirements.txt      # Project dependencies
└── README.md             # Project documentation

Installation

Clone the repository:

git clone https://github.com/freedanfan/mcp_server.git
cd mcp_server

Install dependencies:

pip install -r requirements.txt

Usage

Starting the Server

python mcp_server.py

By default, the server will start on 127.0.0.1:12000. You can customize the host and port using environment variables:

export MCP_SERVER_HOST=0.0.0.0
export MCP_SERVER_PORT=8000
python mcp_server.py

Running the Client

Run the client in another terminal:

python mcp_client.py

If the server is not running at the default address, you can set an environment variable:

export MCP_SERVER_URL="http://your-server-address:port"
python mcp_client.py

API Endpoints

The server provides the following API endpoints:

Root Path (/): Provides server information
API Endpoint (/api): Handles JSON-RPC requests
SSE Endpoint (/sse): Handles SSE connections

MCP Protocol Implementation

Initialization Flow

Client connects to the server via SSE
Server returns the API endpoint URI
Client sends an initialization request with protocol version and capabilities
Server responds to the initialization request, returning server capabilities

Sampling Request

Clients can send sampling requests with prompts:

{
  "jsonrpc": "2.0",
  "id": "request-id",
  "method": "sample",
  "params": {
    "prompt": "Hello, please introduce yourself."
  }
}

The server will return sampling results:

{
  "jsonrpc": "2.0",
  "id": "request-id",
  "result": {
    "content": "This is a response to the prompt...",
    "usage": {
      "prompt_tokens": 10,
      "completion_tokens": 50,
      "total_tokens": 60
    }
  }
}

Closing a Session

Clients can send a shutdown request:

{
  "jsonrpc": "2.0",
  "id": "request-id",
  "method": "shutdown",
  "params": {}
}

The server will gracefully shut down:

{
  "jsonrpc": "2.0",
  "id": "request-id",
  "result": {
    "status": "shutting_down"
  }
}

Development Extensions

Adding New Methods

To add new MCP methods, add a handler function to the MCPServer class and register it in the _register_methods method:

def handle_new_method(self, params: dict) -> dict:
    """Handle new method"""
    logger.info(f"Received new method request: {params}")
    # Processing logic
    return {"result": "success"}

def _register_methods(self):
    # Register existing methods
    self.router.register_method("initialize", self.handle_initialize)
    self.router.register_method("sample", self.handle_sample)
    self.router.register_method("shutdown", self.handle_shutdown)
    # Register new method
    self.router.register_method("new_method", self.handle_new_method)

Integrating AI Models

To integrate actual AI models, modify the handle_sample method:

async def handle_sample(self, params: dict) -> dict:
    """Handle sampling request"""
    logger.info(f"Received sampling request: {params}")
    
    # Get prompt
    prompt = params.get("prompt", "")
    
    # Call AI model API
    # For example: using OpenAI API
    response = await openai.ChatCompletion.acreate(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )
    
    content = response.choices[0].message.content
    usage = response.usage
    
    return {
        "content": content,
        "usage": {
            "prompt_tokens": usage.prompt_tokens,
            "completion_tokens": usage.completion_tokens,
            "total_tokens": usage.total_tokens
        }
    }

Troubleshooting

Common Issues

Connection Errors: Ensure the server is running and the client is using the correct server URL
405 Method Not Allowed: Ensure the client is sending requests to the correct API endpoint
SSE Connection Failure: Check network connections and firewall settings

Logging

Both server and client provide detailed logging. View logs for more information:

# Increase log level
export PYTHONPATH=.
python -m logging -v DEBUG -m mcp_server

References

License

This project is licensed under the MIT License. See the LICENSE file for details.

MCP Server

Integrations

MCP Server

Project Overview

Features

Project Structure

Installation

Usage

Starting the Server

Running the Client

API Endpoints

MCP Protocol Implementation

Initialization Flow

Sampling Request

Closing a Session

Development Extensions

Adding New Methods

Integrating AI Models

Troubleshooting

Common Issues

Logging

References

License

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