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FastAPI-MCP: Turn FastAPI Endpoints into MCP-Standard Tools
What is FastAPI-MCP?
FastAPI-MCP is an open-source tool that can automatically convert the endpoint interfaces of a FastAPI application into MCP-compliant tools with zero configuration. It preserves the request/response schemas as well as the Swagger documentation. Through simple code integration, it enables seamless connection between FastAPI applications and AI systems, supporting AI-driven automation tasks.
For example, if you ask Claude to execute: "Send an email to the user with ID 1111 about XX", Claude will directly call your email-sending API. Without writing code or doing anything manually, it knows exactly how to use this API correctly.
FastAPI-MCP Features
Zero-configuration integration: No complex setup needed — just a few lines of code to automatically convert FastAPI endpoints into MCP tools.
Automatic discovery and conversion: Automatically discovers all endpoints in a FastAPI application and converts them into MCP tools.
Preserves documentation and schemas: Retains the request/response schemas of FastAPI endpoints along with the Swagger documentation, ensuring accuracy during AI invocations.
Flexible deployment: Supports mounting the MCP server directly into a FastAPI application, or deploying it independently.
Customization options: Filter exposed endpoints by operation ID or tags, and adjust tool naming and descriptions.
FastAPI-MCP Installation and Usage
Installation: It is recommended to install using uv, but pip also works.
uv add fastapi-mcp
or
pip install fastapi-mcp
Basic usage: Add the MCP server to a FastAPI application.
from fastapi import FastAPI from fastapi_mcp import FastApiMCP app = FastAPI() mcp = FastApiMCP( app, name="My API MCP", description="My API description", base_url="http://localhost:8000", ) mcp.mount()
Once done, the MCP server will be available at the specified path.
FastAPI-MCP Use Cases
AI-driven automation: Enables AI agents to perform automated tasks via APIs, such as data analysis, content management, e-commerce, and more.
Internal automation: Used for automation workflows within enterprises.
Multi-agent collaboration: Supports AI agents collaborating through standardized APIs.
github:https://github.com/tadata-org/fastapi_mcp
