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MCP Server

by freedanfan

AI/InfrastructureFastAPIJSON-RPCSSEAI Models

The MCP Server enables standardized context interaction between AI models and development environments. It simplifies model deployment, provides efficient API endpoints, and ensures consistency in model input and output.

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What is MCP Server?

The MCP Server is a Python-based implementation of the Model Context Protocol (MCP), built on FastAPI. It facilitates standardized context interaction between AI models and development environments, enhancing scalability and maintainability of AI applications.

How to use MCP Server?

  1. Clone the repository. 2. Install dependencies using pip install -r requirements.txt. 3. Start the server using python mcp_server.py (customize host and port with environment variables if needed). 4. Run the client in another terminal using python mcp_client.py (set MCP_SERVER_URL environment variable if the server is not at the default address).

Key features of MCP Server

  • 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

Use cases of MCP Server

  • Standardized AI model deployment

  • Efficient API endpoints for AI tasks

  • Consistent model input and output management

  • Simplified integration and management of AI tasks

FAQ from MCP Server

How do I customize the server's host and port?

Use the MCP_SERVER_HOST and MCP_SERVER_PORT environment variables.

How do I specify the server URL for the client?

Use the MCP_SERVER_URL environment variable.

What API endpoints are available?

The server provides endpoints for the root path (/), API requests (/api), and SSE connections (/sse).

How can I add new MCP methods?

Add a handler function to the MCPServer class and register it in the _register_methods method.

How can I integrate actual AI models?

Modify the handle_sample method to call the AI model API and return the results in the expected format.