MCP Servers

What is MCP Servers?

MCP Servers is a framework for decoupled real-time agent architectures that connect LangGraph-based AI agents to remote tools via custom MCP (Modular Command Protocol) servers. It enables modular, scalable tool execution using SSE or STDIO transports, and integrates with LangChain and LangGraph to orchestrate LLM workflows. Designed for developers building enterprise-grade AI applications, it also provides infrastructure for real-time tool execution, structured knowledge retrieval, and dynamic agentic interactions for clients like Claude and Cursor.

How to use MCP Servers?

Run tool servers (e.g., math_server.py using STDIO, weather_server.py using SSE) via mcp.run(transport="sse"). Connect agents using MultiServerMCPClient with a dictionary mapping server names to connection parameters, or use raw ClientSession for low‑level control. All interactions use Python’s async/await for non‑blocking I/O.

Key features of MCP Servers

• Decoupled architecture: LangGraph agents orchestrate, MCP servers execute tools.
• Supports both SSE and STDIO transport protocols.
• MultiServerMCPClient enables concurrent connections to multiple tool servers.
• Async I/O for scalable, real‑time tool execution.
• Dynamic tool discovery via MCP handshake and session.list_tools().
• LangChain‑compatible tool wrappers for seamless integration.
• Self‑evolving tool registry API for runtime capability discovery.

Use cases of MCP Servers

• Real‑time multi‑server tool orchestration (math, weather, etc.).
• Enterprise LLM applications with independently scalable tool servers.
• Agent‑to‑agent communication and capability sharing.
• Structured knowledge retrieval and RAG workflows with LangGraph.
• Dynamic tool execution with user approval for AI assistants.