FastAPI-MCP: Expose FastAPI Endpoints as AI Tools

June 30, 2025

Practical Open Source Projects

AI Tools Open Source FastAPI Model Context Protocol API Development

Seamlessly Integrate FastAPI with AI Tools Using FastAPI-MCP

In the rapidly evolving landscape of artificial intelligence, connecting traditional web APIs with advanced AI models, particularly large language models (LLMs), is becoming increasingly crucial. FastAPI-MCP emerges as a powerful open-source solution designed to bridge this gap, enabling developers to effortlessly expose their FastAPI endpoints as Model Context Protocol (MCP) tools.

What is FastAPI-MCP?

FastAPI-MCP is a robust Python library that allows you to transform your existing FastAPI applications into AI-ready services. By implementing the Model Context Protocol, FastAPI-MCP makes your API endpoints discoverable and usable by various AI agents and LLMs. This means your FastAPI functions can serve as 'tools' that AI models can invoke to perform specific actions or retrieve information.

Key Features and Benefits:

Authentication Built-in: One of the standout features of FastAPI-MCP is its seamless integration with FastAPI's native authentication mechanisms. You can secure your MCP endpoints using your existing FastAPI Depends() for authentication and authorization, ensuring that your AI tools operate within secure boundaries.
FastAPI-Native Design: Unlike simple OpenAPI converters, FastAPI-MCP is built from the ground up as a native extension of FastAPI. This 'FastAPI-first' approach ensures deep compatibility and leverages FastAPI's powerful features, including its ASGI interface for efficient communication, eliminating the need for HTTP calls from the MCP to your API.
Zero/Minimal Configuration: Getting started with FastAPI-MCP is remarkably straightforward. With just a few lines of code, you can mount an MCP server directly to your FastAPI application, making it instantly available for AI consumption. This minimal setup significantly reduces development overhead.
Preserves Schemas and Documentation: FastAPI-MCP accurately preserves the schemas of your request and response models, identical to how they appear in FastAPI's interactive Swagger documentation. This ensures that AI models receive precise information about your API's input and output structures.
Flexible Deployment: Whether you prefer to mount the MCP server directly within your existing FastAPI application or deploy it as a separate service, FastAPI-MCP offers the flexibility to suit your architectural needs.

Practical Applications:

Imagine an LLM needing to retrieve real-time inventory data from your e-commerce platform. With FastAPI-MCP, you can expose your get_product_inventory endpoint as an MCP tool. The LLM can then interact with this tool to fetch the necessary data directly, enabling more intelligent and dynamic responses.

This project is ideal for developers building AI-powered applications, creating custom tools for LLMs, or seeking to enhance their existing FastAPI services with AI capabilities. Its open-source nature means a vibrant community and continuous improvements, making it a valuable addition to any developer's toolkit.

Getting Started:

Installation is simple via uv or pip:

uv add fastapi-mcp
# or
pip install fastapi-mcp

For basic usage, integrate it directly into your FastAPI app:

from fastapi import FastAPI
from fastapi_mcp import FastApiMCP

app = FastAPI()

mcp = FastApiMCP(app)

# Mount the MCP server directly to your FastAPI app
mcp.mount()

Your auto-generated MCP server will then be accessible at https://app.base.url/mcp.

FastAPI-MCP provides comprehensive documentation and examples to help you explore its full potential. By simplifying the connection between traditional APIs and the burgeoning world of AI, FastAPI-MCP empowers developers to build more intelligent and interactive applications with ease.

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