Overview
What is MCP Server Development Framework?
A professional framework for enterprise-level Model Context Protocol (MCP) tool development, integrating FastAPI and FastAPI-MCP to automate converting traditional APIs into AI-callable MCP tools.
How to use MCP Server Development Framework?
Use uv as package manager; run make install to set up the environment and make dev to start the example service. Connect to the MCP endpoint at http://localhost:5000/mcp with an MCP client.
Key features of MCP Server Development Framework
- Automatically converts FastAPI endpoints into MCP tools
- Supports interface-implementation separation for testing and environment switching
- Leverages FastAPI’s dependency injection for flexible component decoupling
- Provides complete development pipeline from development to deployment
- Includes example-driven documentation and multi-level testing support
Use cases of MCP Server Development Framework
- Teams building AI tools that need to expose existing APIs as MCP endpoints
- Developers transforming FastAPI-based APIs into standardized AI-callable tools
- Organizations adopting a standardized, interface‑based microservice architecture
FAQ from MCP Server Development Framework
What runtime does it require?
Python 3.10 or later, with FastAPI and FastAPI-MCP as core dependencies. The framework uses uv for package management.
How does dependency injection work in this framework?
FastAPI’s Depends function injects service implementations into API endpoints. It supports function, class, and nested dependencies, enabling environment-based switching between mock and real implementations.
Can I use existing FastAPI endpoints without modification?
Yes. FastAPI-MCP automatically exposes any FastAPI endpoint as an MCP tool. You only need to mount the MCP service via mcp.mount(mount_path="/mcp") and optionally set an explicit operation_id.
Where can I find example code and best practices?
The README provides a full service interface example, dependency injection configurations, and recommended MCP tool naming conventions (e.g., predict_sentiment, find_nearby_parking).
