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LangChain Agent with MCP Servers

@esakrissa

Repository

Basic information

Category

AI & Agents

Transports

stdio

Author

esakrissa

About LangChain Agent with MCP Servers

LangChain Agent with MCP Servers: Using LangChain MCP Adapters for tool integration.

#langchain#mcp#tool-integration

LangChain Agent with MCP Servers config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "langchain-mcp-esakrissa": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        ".venv"
      ]
    }
  }
}

LangChain Agent with MCP Servers tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is LangChain Agent with MCP Servers?

A LangChain agent that uses the Model Context Protocol (MCP) to integrate with external services such as Tavily Search, a mock weather service, and a math expression evaluator. It is built for developers who want a modular, LangGraph-powered agent with graceful shutdown and easy extensibility.

How to use LangChain Agent with MCP Servers?

Install dependencies, create a virtual environment, and add API keys for OpenAI and Tavily to a .env file. Run python src/agent.py from the command line and enter a query when prompted. The agent automatically selects and invokes the appropriate MCP‑server tool.

Key features of LangChain Agent with MCP Servers

  • Integrates with Tavily Search, weather, and math MCP servers
  • Uses LangGraph’s ReAct agent pattern for dynamic tool selection
  • Graceful shutdown via signal handling and subprocess tracking
  • Robust error handling throughout the application
  • Modular design makes adding new MCP servers straightforward

Use cases of LangChain Agent with MCP Servers

  • Perform web searches and retrieve news via Tavily
  • Get mock weather information for any location
  • Evaluate mathematical expressions
  • Demonstrate a LangChain agent that dynamically calls external MCP tools

Frequently asked questions

1

What runtime dependencies are required?

Python 3.8+, a virtual environment, and the dependencies listed in `setup.py`. API keys for OpenAI and Tavily must be stored in a `.env` file.

2

How can I add a new MCP server?

Create a new file in `src/mcpserver/` with proper signal handling, expose it in `src/mcpserver/__init__.py`, and add its configuration in `src/agent.py`.

3

What transport or authentication does the agent use?

MCP servers run as subprocesses and communicate via stdin/stdout. Authentication is handled via API keys passed in the `.env` file.

4

Does the agent support graceful shutdown?

Yes. It captures SIGINT and SIGTERM, tracks all child processes, and ensures they terminate cleanly. A shutdown flag prevents new operations during teardown, and asyncio allows in‑progress tasks to finish when possible.

5

What services are available out‑of‑the-box?

Tavily Search (web and news), a mock weather server, and a math expression evaluator.

Comments

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