FastAPI-MCP

FastAPI‑MCP transforms a conventional FastAPI application into a fully‑featured Model Context Protocol (MCP) server with minimal friction. By mounting the MCP directly onto an existing FastAPI app, developers can expose every endpoint as a callable tool for AI assistants such as Claude. This eliminates the need to maintain separate API servers or write custom adapters, allowing teams to leverage their current FastAPI codebase while opening it up to intelligent agents.

The server automatically preserves the schemas of request and response models, ensuring that each MCP tool carries precise type information. This is critical for AI assistants to generate correct calls and validate responses. Additionally, the original Swagger documentation is retained verbatim, so developers can continue to rely on familiar OpenAPI docs while AI agents consume the same metadata. Authentication is not an afterthought; FastAPI‑MCP integrates seamlessly with FastAPI’s dependency injection system, enabling for OAuth2, JWT, or any custom auth scheme to protect MCP endpoints.

FastAPI‑MCP’s ASGI transport means that calls from the AI assistant are routed directly through FastAPI’s asynchronous interface, avoiding costly HTTP round‑trips and reducing latency. The tool also offers flexible deployment options: mount the MCP on the same app for a single‑service architecture, or deploy it separately if isolation is preferred. This duality gives teams control over scaling and security boundaries without sacrificing functionality.

In practice, FastAPI‑MCP is invaluable for developers building AI‑powered workflows. For example, a data science platform can expose model training endpoints as MCP tools, allowing an assistant to trigger jobs, retrieve metrics, and adjust hyperparameters on the fly. Similarly, a SaaS product can expose its billing API as MCP tools so that an AI concierge can manage subscriptions, generate invoices, or troubleshoot usage issues autonomously. By turning any FastAPI service into a first‑class MCP provider, teams accelerate the integration of AI assistants across domains while keeping their existing infrastructure intact.