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MCP Multi-Server Demo with SSE Transport

@fredrikp999

リポジトリ

MCP Multi-Server Demo with SSE Transport について

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

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

基本情報

カテゴリ

AI とエージェント

ランタイム

python

トランスポート

stdio

公開者

fredrikp999

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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.

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