Fastify MCP – A Seamless Model Context Protocol Integration for Fastify

Fastify MCP solves a common pain point for developers building AI‑powered web services: wiring the Model Context Protocol (MCP) into an existing Fastify application without reinventing session handling, transport support, or resource registration. By exposing a ready‑made plugin that wraps the MCP SDK’s streamable HTTP and legacy SSE transports, it lets teams focus on defining tools, resources, and prompts while Fastify manages routing, middleware, and connection life‑cycle.

The core of the server is a lightweight MCP instance that can be configured with a name and version. From there, developers register any number of tools (functions or services the assistant can call) and resources (persistent data or state). The plugin then attaches the MCP endpoint to a Fastify route, automatically handling the HTTP headers, body streaming, and event callbacks required by MCP. Because Fastify is known for its ultra‑fast routing and low overhead, the resulting service can handle high concurrency with minimal latency—a critical factor when assistants need to respond in real time.

Key capabilities include:

• Dual transport support – The plugin works with both the modern streamable HTTP transport and the older HTTP+SSE, ensuring backward compatibility while encouraging adoption of newer standards.
• Stateful and stateless modes – Developers can opt for a fully stateful server that tracks session data across requests, or a stateless one that treats each request independently. This flexibility allows integration into micro‑service architectures where session persistence may be handled elsewhere.
• In‑memory session management – The built‑in class maintains a mapping of active session IDs to transport instances, emitting events for connection lifecycle changes. This eliminates the need for custom session stores and aligns with MCP SDK recommendations.
• Event hooks – By listening to , , and events, developers can log activity, trigger cleanup routines, or integrate with monitoring tools without touching the core MCP logic.

Real‑world use cases span from internal tooling to public APIs:

• AI‑augmented customer support – A Fastify server can expose a knowledge base as a resource and provide a tool that queries support tickets, allowing an assistant to retrieve contextually relevant information in real time.
• Code generation services – Tools that invoke language models or compile code can be registered, while resources hold user project data. The MCP endpoint becomes a single entry point for all assistant interactions.
• Chatbot platforms – By running Fastify MCP behind a load balancer, multiple assistants can share the same backend while each session remains isolated and traceable.

Integration into AI workflows is straightforward. Once the MCP endpoint is live, any client that speaks the Model Context Protocol—Claude, OpenAI’s new tools API, or custom agents—can connect, authenticate via session IDs, and start sending prompts. The server handles streaming responses, tool calls, and resource queries transparently, allowing developers to write business logic in pure JavaScript/TypeScript without worrying about protocol intricacies.

What sets Fastify MCP apart is its minimal footprint coupled with robust session handling. It removes boilerplate, supports the latest transport spec out of the box, and provides a clear event API for observability. For developers already comfortable with Fastify’s ecosystem, this plugin offers an effortless bridge to the evolving MCP landscape, enabling rapid deployment of sophisticated AI assistants that can call tools, access resources, and maintain state across interactions.