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Python 从0到1构建MCP Server & Client

@GobinFan

代码仓库

基本信息

分类

AI 与智能体

传输方式

stdio

作者

GobinFan

关于 Python 从0到1构建MCP Server & Client

支持查询主流agent框架技术文档的MCP server（支持stdio和sse两种传输协议）, 支持 langchain、llama-index、autogen、agno、openai-agents-sdk、mcp-doc、camel-ai 和 crew-ai

#agent#mcp#openai#llm#mcp-server#mcp-client

Python 从0到1构建MCP Server & Client 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "python-mcp-server-client": {
      "command": "uv",
      "args": [
        "init",
        "mcp-server"
      ]
    }
  }
}

Python 从0到1构建MCP Server & Client 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 Python 从0到1构建MCP Server & Client?

A hands-on tutorial and reference implementation for building Model Context Protocol (MCP) servers and clients in Python from scratch. It demonstrates how to expose a documentation-search tool through both local (Stdio) and remote (SSE) transports, and how to connect that server to MCP clients like Cursor, Cline, or a custom Python client.

How to use Python 从0到1构建MCP Server & Client?

Install UV, create a project directory, install dependencies (mcp[cli], httpx), and write a main.py file that defines the MCP server with the get_docs tool. Run the server locally with uv run main.py for Stdio, or uv run main.py --host 0.0.0.0 --port 8020 for SSE. Configure clients (Cline, Cursor) by adding a JSON block pointing uv --directory <path> run main.py to the MCP server definition.

Key features of Python 从0到1构建MCP Server & Client

  • Implements both Stdio (local) and SSE (remote) transport protocols
  • Provides a get_docs tool that searches and retrieves documentation for eight popular AI libraries
  • Uses the Serper API for Google search and BeautifulSoup for web page parsing
  • Includes ready-to-use client configurations for Cursor and Cline
  • Ships a complete custom Python MCP client using the OpenAI SDK
  • Requires only the mcp CLI package, httpx, and BeautifulSoup as dependencies

Use cases of Python 从0到1构建MCP Server & Client

  • Quickly look up the latest LangChain, LlamaIndex, or CrewAI documentation from an AI assistant
  • Build a local MCP server that agents (Claude, Cursor) can query for up-to-date framework docs
  • Learn how to migrate from Function Call to MCP for tool calling across different LLM providers
  • Deploy a cloud-hosted SSE-based MCP server that multiple remote clients can connect to

常见问题

1

What problem does this MCP server solve compared to traditional Function Call?

Before MCP, different LLM providers had inconsistent Function Call formats and each API tool required its own input/output wrapping. MCP acts as a universal "USB-C", standardizing function call formats across providers and unifying tool packaging.

2

What dependencies and runtime are required?

Python, the `uv` package manager, and the `mcp[cli]`, `httpx`, and `BeautifulSoup` Python packages. A Serper API key must be set in the `SERPER_API_KEY` environment variable.

3

Which AI framework documentation sources are supported?

LangChain, LlamaIndex, AutoGen, Agno, OpenAI Agents SDK, MCP documentation, CAMEL-AI, and CrewAI.

4

What transport protocols are available?

Stdio for local use (requires CLI installation) and SSE (Server-Sent Events) for cloud deployment over HTTP long connections.

5

How do I configure the server with Cursor or Cline?

Create a `.cursor/mcp.json` file (Cursor) or configure the Cline plugin settings with the JSON: `{"mcpServers": {"mcp-server": {"command": "uv", "args": ["--directory", "<path>", "run", "main.py"]}}}`.

评论

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