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FastAPI MCP Server + LangChain Client Example

@SDCalvo

代码仓库

基本信息

分类

AI 与智能体

许可证

Apache-2.0 license

传输方式

stdio

作者

SDCalvo

关于 FastAPI MCP Server + LangChain Client Example

Example project demonstrating how to expose FastAPI endpoints as Model Context Protocol (MCP) tools using `fastapi-mcp`. Includes a basic LangChain agent (`langchain_client.py`) that connects to the local FastAPI server via HTTP/SSE using `langchain-mcp-adapters` to discover and

FastAPI MCP Server + LangChain Client Example 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "sse-mcp-and-langchain-client-example": {
      "command": "uv",
      "args": [
        "init",
        "#",
        "If",
        "pyproject.toml",
        "doesnt exist"
      ]
    }
  }
}

FastAPI MCP Server + LangChain Client Example 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 Server + LangChain Client Example?

This example project demonstrates how to expose FastAPI endpoints as Model Context Protocol (MCP) tools using the fastapi-mcp library. It includes a LangChain agent client (langchain_client.py) that connects to the local FastAPI server via HTTP/SSE using langchain-mcp-adapters to discover and use the exposed tools. It is intended for learning and testing, runnable locally and connectable from MCP clients like Cursor’s agent.

How to use FastAPI MCP Server + LangChain Client Example?

Clone the repository, install uv, create a virtual environment, and install dependencies (fastapi, uvicorn, fastapi-mcp, langchain-mcp-adapters, langgraph, langchain-openai, python-dotenv). Create a .env file with your OpenAI API key. In one terminal, run uvicorn main:app --reload --port 8000. In a second terminal, run uv run python langchain_client.py. Optionally, test with the MCP Inspector via npx @modelcontextprotocol/inspector or configure Cursor using a .cursor/mcp.json with the server URL http://127.0.0.1:8000/mcp.

Key features of FastAPI MCP Server + LangChain Client Example

  • Exposes FastAPI endpoints as MCP tools automatically via fastapi-mcp.
  • LangChain agent discovers and calls tools through SSE transport.
  • Supports Cursor editor integration via HTTP-based MCP configuration.
  • Includes a ready‑to‑run example with sample endpoints and queries.
  • Demonstrates optional MCP Inspector for interactive tool testing.

Use cases of FastAPI MCP Server + LangChain Client Example

  • Learning how to turn a FastAPI application into an MCP server.
  • Rapid prototyping of MCP tools using familiar FastAPI route definitions.
  • Integrating FastAPI‑backed tools into AI agents like LangChain or Cursor.
  • Testing and debugging MCP server features locally before deployment.

常见问题

1

What prerequisites are needed?

Python 3.10 or higher, the `uv` package manager, Node.js and npm (for the optional MCP Inspector), and an OpenAI API key (for the LangChain client). Git is required for cloning the repository.

2

How are MCP tool names determined?

MCP tool names are derived from the FastAPI route’s `operation_id`. If not set explicitly, FastAPI generates an automatic name (e.g., `read_root__get`). Setting explicit `operation_id` values on routes is recommended for clearer tool names.

3

What if a port is already in use?

Use `netstat -ano | findstr "<PORT>"` (Windows) to find the process ID, then `taskkill /F /PID <PID>` to terminate it. Sometimes a short wait or restarting the IDE is needed for the OS to fully release the port.

4

How do I add new routes after the server is mounted?

Routes defined after calling `mcp.mount()` are not automatically included. Call `mcp.setup_server()` again after defining the new routes to refresh the tool list.

5

Does this server support authentication?

Yes. `fastapi-mcp` supports FastAPI dependencies for authorization and includes OAuth 2 support. Basic token passthrough can be configured via `mcp-remote` with headers, or server‑side dependencies can be added using `AuthConfig`.

评论

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