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.
@fredrikp999
Basic information
Example of using MCP servers, both over sdio + sse. Also using langchain-mcp
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcp-sse-langchain-example": {
"command": "python",
"args": [
"main.py"
]
}
}
}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.
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.
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.
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.
Python 3.8+ and an OpenAI API key. Dependencies are installed via `requirements.txt`.
The weather data is simulated (no real API). All data is local to the demo.
The math server uses stdio transport; the weather server uses SSE transport (HTTP-based server‑to‑client push).
Only the OpenAI API key (set in `.env`) is required for the agent. No additional auth for the MCP servers.
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