MCP Multi-Server Demo with SSE Transport

@fredrikp999

关于 MCP Multi-Server Demo with SSE Transport

Example of using MCP servers, both over sdio + sse. Also using langchain-mcp

#mcp #sse #langchain #multi-server #demo

基本信息

分类

AI 与智能体

运行时

python

传输方式

stdio

发布者

fredrikp999

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-sse-langchain-example": {
      "command": "python",
      "args": [
        "main.py"
      ]
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is MCP Multi-Server Demo with SSE Transport?

A demonstration project that uses the Model Context Protocol (MCP) to connect multiple servers via different transport methods (stdio and Server-Sent Events). It integrates with LangChain and OpenAI to create an agent that can use tools from both a math server and a weather server.

How to use MCP Multi-Server Demo with SSE Transport?

Clone the repository, install dependencies with pip install -r requirements.txt, and set your OpenAI API key in a .env file. Run python main.py to start the weather server on port 8000, connect to both servers, and execute example queries.

Key features of MCP Multi-Server Demo with SSE Transport

Math server with basic arithmetic operations (add, multiply)
Weather server providing simulated weather for different locations
MultiServerMCPClient to connect to servers with different transports
Integration with LangChain and OpenAI for agent functionality
Example queries for math and weather
Extensible: add more tools, servers, or real APIs

Use cases of MCP Multi-Server Demo with SSE Transport

Demonstrating how to use MCP with both stdio and SSE transports
Building a LangChain agent that can call tools from multiple MCP servers
Prototyping an assistant that combines math calculations and weather lookups
Learning how to extend MCP with custom server capabilities

FAQ from MCP Multi-Server Demo with SSE Transport

What does this demo do?

It shows how to connect multiple MCP servers using different transports (stdio and SSE) and create a LangChain agent that can use tools from both.

What are the dependencies?

Python 3.8+ and an OpenAI API key. Dependencies are installed via requirements.txt.

Where does the data live?

The weather data is simulated (no real API). All data is local to the demo.

What transport methods are used?

The math server uses stdio transport; the weather server uses SSE transport (HTTP-based server‑to‑client push).

How is authentication handled?

Only the OpenAI API key (set in .env) is required for the agent. No additional auth for the MCP servers.

评论

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