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MCP Server

@freedanfan

Repository

About MCP Server

基于 FastAPI 和 MCP（模型上下文协议），实现 AI 模型与开发环境 之间的标准化上下文交互，提升 AI 应用的可扩展性和可维护性。

Basic information

Category

Other

License

MIT

Runtime

python

Transports

stdio

Publisher

freedanfan

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp-server-freedanfan": {
      "command": "python",
      "args": [
        "mcp_server.py"
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is MCP Server?

MCP Server is a Python-based implementation of the Model Context Protocol (MCP) built on FastAPI. It enables standardized context interaction between AI models and development environments by providing efficient API endpoints, simplifying model deployment, and ensuring consistency in model input and output.

How to use MCP Server?

Clone the repository, install dependencies with pip install -r requirements.txt, then start the server with python mcp_server.py (defaults to 127.0.0.1:12000). Host and port can be customized via environment variables MCP_SERVER_HOST and MCP_SERVER_PORT. Run the included test client with python mcp_client.py.

Key features of MCP Server

  • JSON-RPC 2.0 request-response communication
  • Server-Sent Events (SSE) support for real-time notifications
  • Modular architecture for easy extension
  • Asynchronous processing with FastAPI
  • Complete test client implementation

Use cases of MCP Server

  • Integrating AI models with standardized context interaction
  • Building maintainable AI applications with unified input/output
  • Real-time communication between AI services and clients via SSE
  • Rapid prototyping of MCP-based services using the test client

FAQ from MCP Server

What does MCP Server do that other solutions don’t?

It provides a Python-based implementation of the Model Context Protocol, offering standardized JSON-RPC 2.0 endpoints, SSE connections, and a modular design that simplifies AI model deployment and integration.

What are the runtime requirements?

Python with dependencies listed in requirements.txt. The server runs as a standalone Python application using FastAPI.

Where does data live?

The server processes requests in‑memory and returns responses. No permanent storage is mentioned in the README.

How do I handle connection or SSE failures?

Ensure the server is running and the client uses the correct server URL. Check network connections and firewall settings for SSE connection issues.

How do I extend MCP Server?

Register new method handlers in the MCPServer._register_methods method, and optionally integrate actual AI models in the handle_sample method (e.g., by calling an external AI API).

Comments

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