FastAPI MCP Server with LangChain Client

The Sse Mcp And LangChain Client Example is a turnkey showcase that bridges the Model Context Protocol (MCP) with the popular LangChain framework. It demonstrates how a FastAPI application can expose its REST endpoints as MCP tools, enabling an AI assistant to discover and invoke these capabilities dynamically. By leveraging the library, each HTTP route becomes a first‑class tool that can be queried over SSE (Server‑Sent Events), giving the client real‑time, streaming responses that fit naturally into conversational AI workflows.

For developers building intelligent agents, this setup solves a common pain point: the need to expose arbitrary business logic or external services as conversationally accessible tools without rewriting them for each platform. Instead of creating bespoke adapters, the server automatically translates FastAPI routes into MCP tool descriptors. The client side, powered by , discovers these tools at runtime and lets a LangChain agent orchestrate them as part of its reasoning chain. This tight integration means agents can call out to domain APIs, perform calculations, or fetch data—all while maintaining a natural dialogue with the user.

Key features include:

• Automatic MCP Tool Generation – FastAPI endpoints are introspected and exposed as tools with clear metadata, parameters, and documentation.
• SSE‑Based Streaming – Tool responses can be streamed back to the agent, allowing for incremental generation and better latency handling.
• LangChain Agent Compatibility – The example agent demonstrates how to use the discovered tools within a prompt‑based workflow, showcasing tool selection and invocation logic.
• Optional MCP Inspector – A lightweight UI lets developers manually test tools, view their signatures, and validate integration before deploying the agent.

Real‑world scenarios that benefit from this pattern are plentiful. A customer support bot can call an internal ticketing API to create or update tickets on the fly. An analytics assistant could query a FastAPI endpoint that runs complex SQL or machine‑learning models, returning results in a conversational format. Even simple utilities like greeting services or data validation can be turned into conversational actions with minimal effort.

By exposing existing FastAPI services as MCP tools, developers gain a powerful, low‑friction pathway to extend AI assistants into their own ecosystems. The server’s ability to stream responses, coupled with LangChain’s flexible agent architecture, provides a robust foundation for building sophisticated, context‑aware AI applications that can seamlessly interact with any backend logic.