Math MCP Server

Overview of the Langgraph MCP Integration

Langgraph’s Multi‑Client Protocol (MCP) integration transforms a conventional LangGraph workflow into a fully modular, tool‑centric architecture. By exposing a lightweight Math Server that implements elementary arithmetic operations as MCP tools, the integration demonstrates how an AI assistant can delegate domain‑specific logic to dedicated services while keeping the conversational model focused on reasoning. This approach is particularly valuable for developers who need to scale AI applications across multiple specialized services without rewriting the core agent logic.

The MCP server, named Math, registers two callable functions— and —and runs on a stdio transport. This minimal setup allows any client that understands MCP to discover the available tools, instantiate them on demand, and invoke them with JSON‑serialised arguments. The server’s single responsibility—performing arithmetic—illustrates how more complex services (e.g., database queries, image generation, or API wrappers) can be added with the same pattern. Because each tool is isolated in its own process, failures or performance bottlenecks do not cascade to the main agent, enhancing reliability and fault tolerance.

In practice, a single‑server client launches the Math Server as a subprocess and establishes an asynchronous MCP session. The LangGraph ReAct agent receives the user prompt, reasons about the required steps, and emits tool calls such as or . The MCP client forwards these calls to the server, obtains results, and feeds them back into the agent’s internal state. The final response—“The result is 96”—is produced without any hard‑coded logic in the agent itself. This separation of concerns makes it straightforward to swap or upgrade individual tools without touching the higher‑level reasoning code.

Key features of this integration include:

• Tool discovery and registration: The server advertises its capabilities automatically, allowing clients to introspect available functions at runtime.
• Transport agnostic communication: Although the example uses stdio, MCP supports other transports (e.g., HTTP or WebSocket) for distributed deployments.
• Scalable architecture: Multiple servers can be orchestrated behind a single client, enabling horizontal scaling and load balancing.
• ReAct agent synergy: The LangGraph agent naturally interprets tool calls, making the workflow feel seamless to developers and users alike.

Typical use cases span educational tutoring systems that delegate calculations to a math server, data‑driven applications where domain experts expose specialized analytical tools as MCP services, and multi‑service bots that coordinate between weather APIs, calendar schedulers, and natural language models. By decoupling reasoning from execution, Langgraph’s MCP integration empowers developers to build robust, extensible AI assistants that can grow with new capabilities without sacrificing maintainability.