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FastAPI MCP 服务器

@purity3

代码仓库

基本信息

分类

开发工具

许可证

MIT

传输方式

stdio

作者

purity3

关于 FastAPI MCP 服务器

暂无概览

FastAPI MCP 服务器 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "fastapi-mcp-server": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        ".venv"
      ]
    }
  }
}

FastAPI MCP 服务器 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is 🚀 FastAPI MCP 服务器?

The 🚀 FastAPI MCP 服务器 is a lightweight, high-performance implementation of the Model Context Protocol (MCP) for large language models. Built on FastAPI, it provides Server-Sent Events (SSE) communication, intelligent tool registration, and session management for multi-user, multi-model concurrent interactions. It is designed for developers building AI-powered applications that need a standardized backend interface.

How to use 🚀 FastAPI MCP 服务器?

Install Python 3.13+ and optionally uv. Clone the repository, create a virtual environment, and install dependencies with pip install -e .. Configure environment variables in a .env file and create the database file database/session.db. Start the server with python -m main or start. It runs by default on http://localhost:8000. Custom tool functions can be added in the tools/ directory and registered in server.py.

Key features of 🚀 FastAPI MCP 服务器

  • Integrates FastAPI’s high performance with the MCP protocol
  • Efficient SSE-based real-time communication with millisecond latency
  • Multi-user session isolation for data separation
  • Flexible authentication via token, path, or query parameters
  • Fully asynchronous architecture supporting high concurrency
  • Intelligent tool registration system to extend model capabilities

Use cases of 🚀 FastAPI MCP 服务器

  • Building a custom AI assistant backend with standardized model interactions
  • Enabling real-time, low-latency tool calls from LLM applications
  • Developing multi-tenant or multi-user AI-powered services
  • Creating a pluggable tool ecosystem for model-based workflows

常见问题

1

How does authentication work?

The server supports multiple authentication methods, including token, path, and query parameters. You can customize the API key verification logic in `auth/credential.py`.

2

What are the runtime dependencies?

Python 3.13 or higher is required. An asynchronous database (e.g., SQLite) is optional but recommended for session persistence. The uv package manager is recommended for installation.

3

Where does session data live?

Session data is stored in `database/session.db` (SQLite by default). The database file is created manually or automatically on first run. The `DATABASE_URL` environment variable can be used to configure a different database.

4

What transport protocol is used?

The server uses Server-Sent Events (SSE) for real-time, unidirectional data streaming from server to client, enabling low-latency communication.

5

Can I run the server in a container?

Container support is planned for future versions (Docker image and docker-compose). Currently, the server runs directly with Python

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