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MCP Client-Server Python Example

@sofianhw

代码仓库

基本信息

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其他

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stdio

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sofianhw

关于 MCP Client-Server Python Example

Basic implementation mcp client server

MCP Client-Server Python Example 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-client-server-python": {
      "command": "uv",
      "args": [
        "sync"
      ]
    }
  }
}

MCP Client-Server Python Example 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

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 Client-Server Python Example?

This project demonstrates a simple client-server implementation of the Model Context Protocol (MCP), an open protocol by Anthropic that enables large language models to interact with external tools and resources. It is intended for developers learning how to build MCP servers and clients using Python.

How to use MCP Client-Server Python Example?

Install dependencies with uv sync, create a .env file with OPENAI_API_KEY and MCP_SSE_URL, start the server with uv run server.py --host 0.0.0.0 --port 8080, then run the client with uv run client.py. The client will list available tools and start an interactive chat loop. Type quit to exit.

Key features of MCP Client-Server Python Example

  • Exposes tools (e.g., addition) and resources (e.g., greetings) via SSE.
  • Client connects to server and lists available tools.
  • Uses OpenAI GPT models to process queries and call tools.
  • Built with Python 3.12+ and the MCP Python SDK.
  • Uses SSE (Server-Sent Events) for transport.
  • Managed with uv for fast dependency resolution.

Use cases of MCP Client-Server Python Example

  • Learning how to build an MCP server and client from scratch.
  • Prototyping an agentic workflow where an LLM calls external tools.
  • Testing SSE-based communication between MCP components.
  • Demonstrating integration of MCP with OpenAI’s chat completions API.
  • Educational reference for implementing the Model Context Protocol.

常见问题

1

What is MCP?

MCP stands for Model Context Protocol, an open protocol introduced by Anthropic that allows language models to interact with external tools, APIs, and resources in a standardized and secure way. It supports agentic workflows and multi-channel communication.

2

What are the runtime requirements?

Python 3.12 or higher, along with the MCP Python SDK, OpenAI Python SDK, Uvicorn, python-dotenv, and `uv` as the package installer.

3

How do I set up the server?

Create a `.env` file with an `OPENAI_API_KEY` and the server SSE URL (`MCP_SSE_URL=http://localhost:8080/sse`), then run `uv run server.py --host 0.0.0.0 --port 8080`. The server exposes tools and resources at the `/sse` endpoint.

4

How do I run the client?

In a separate terminal, run `uv run client.py`. The client will connect to the server, list available tools, and start an interactive prompt. Type your query, and the client uses OpenAI to decide whether to call a server tool.

5

How do I stop the client?

Type `quit` at the prompt to exit the interactive chat loop.

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