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python-mcp服务器客户端

python-mcp服务器客户端

作者：GobinFan150 星标更新：2025-05-05
安装使用来源：spider==github.com

项目介绍

MseeP.ai 安全评估徽章

使用Python从零开始构建MCP服务器和客户端

中文 英文

简介

MCP服务器实现了模型上下文协议（MCP），旨在为AI模型提供一个标准化接口，以连接外部数据源和工具，如文件系统、数据库或API。

image

MCP的优势

在MCP出现之前，AI调用工具大多通过函数调用来完成，存在以下问题：

  1. 不同大型模型制造商的函数调用格式不一致
  2. 大量API工具的输入输出格式不一致，封装管理复杂

image

MCP相当于统一的USB-C，不仅统一了不同大型模型制造商的函数调用格式，还统一了相关工具的封装。

MCP传输协议

目前，MCP支持两种主要传输协议：

  1. Stdio传输协议

    • 用于本地使用
    • 用户需要在本地安装命令行工具
    • 对操作环境有特定要求

  2. SSE（服务器发送事件）传输协议

    • 用于云服务部署
    • 基于HTTP长连接实现

项目结构

MCP服务器

  • Stdio传输协议（本地）
  • SSE传输协议（远程）

MCP客户端

  • 自建客户端（Python）
  • Cursor
  • Cline

环境配置

1. 安装UV包

MacOS/Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

2. 初始化项目

# 创建项目目录
uv init mcp-server
cd mcp-server

# 创建并激活虚拟环境
uv venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

# 安装依赖
uv add "mcp[cli]" httpx

# 创建服务器实现文件
touch main.py

构建工具功能

image

为了使大型模型能够访问市场上主流框架的技术文档，我们主要通过用户输入查询结合Google搜索特定站点域名来搜索相关网页，并解析提取相关网页的文本内容后返回。

1. 构建相关文档映射字典

docs_urls = {
    "langchain": "python.langchain.com/docs",
    "llama-index": "docs.llamaindex.ai/en/stable",
    "autogen": "microsoft.github.io/autogen/stable",
    "agno": "docs.agno.com",
    "openai-agents-sdk": "openai.github.io/openai-agents-python",
    "mcp-doc": "modelcontextprotocol.io",
    "camel-ai": "docs.camel-ai.org",
    "crew-ai": "docs.crewai.com"
}

2. 构建MCP工具

import json
import os
import httpx
from bs4 import BeautifulSoup
from mcp import tool

async def search_web(query: str) -> dict | None:
    payload = json.dumps({"q": query, "num": 3})

    headers = {
        "X-API-KEY": os.getenv("SERPER_API_KEY"),
        "Content-Type": "application/json",
    }

    async with httpx.AsyncClient() as client:
        try:
            response = await client.post(
                SERPER_URL, headers=headers, data=payload, timeout=30.0
            )
            response.raise_for_status()
            return response.json()
        except httpx.TimeoutException:
            return {"organic": []}

async def fetch_url(url: str):
    async with httpx.AsyncClient() as client:
        try:
            response = await client.get(url, timeout=30.0)
            soup = BeautifulSoup(response.text, "html.parser")
            text = soup.get_text()
            return text
        except httpx.TimeoutException:
            return "Timeout error"

@tool()
async def get_docs(query: str, library: str):
    """
    搜索给定查询和库的最新文档。
    支持 langchain、llama-index、autogen、agno、openai-agents-sdk、mcp-doc、camel-ai 和 crew-ai。

    参数:
    query: 要搜索的查询 (例如 "React Agent")
    library: 要搜索的库 (例如 "agno")

    返回:
    文档中的文本
    """
    if library not in docs_urls:
        raise ValueError(f"Library {library} not supported by this tool")

    query = f"site:{docs_urls[library]} {query}"
    results = await search_web(query)
    if len(results["organic"]) == 0:
        return "No results found"

    text = ""
    for result in results["organic"]:
        text += await fetch_url(result["link"])

    return text

封装MCP服务器（基于Stdio协议）

1. MCP服务器（Stdio）

# main.py
from mcp.server.fastmcp import FastMCP
from dotenv import load_dotenv
import httpx
import json
import os
from bs4 import BeautifulSoup
from typing import Any
import httpx
from mcp.server.fastmcp import FastMCP
from starlette.applications import Starlette
from mcp.server.sse import SseServerTransport
from starlette.requests import Request
from starlette.routing import Mount, Route
from mcp.server import Server
import uvicorn

load_dotenv()

mcp = FastMCP("Agentdocs")

USER_AGENT = "Agentdocs-app/1.0"
SERPER_URL = "https://google.serper.dev/search"

docs_urls = {
    "langchain": "python.langchain.com/docs",
    "llama-index": "docs.llamaindex.ai/en/stable",
    "autogen": "microsoft.github.io/autogen/stable",
    "agno": "docs.agno.com",
    "openai-agents-sdk": "openai.github.io/openai-agents-python",
    "mcp-doc": "modelcontextprotocol.io",
    "camel-ai": "docs.camel-ai.org",
    "crew-ai": "docs.crewai.com"
}

async def search_web(query: str) -> dict | None:
    payload = json.dumps({"q": query, "num": 2})

    headers = {
        "X-API-KEY": os.getenv("SERPER_API_KEY"),
        "Content-Type": "application/json",
    }

    async with httpx.AsyncClient() as client:
        try:
            response = await client.post(
                SERPER_URL, headers=headers, data=payload, timeout=30.0
            )
            response.raise_for_status()
            return response.json()
        except httpx.TimeoutException:
            return {"organic": []}

async def fetch_url(url: str):
    async with httpx.AsyncClient() as client:
        try:
            response = await client.get(url, timeout=30.0)
            soup = BeautifulSoup(response.text, "html.parser")
            text = soup.get_text()
            return text
        except httpx.TimeoutException:
            return "Timeout error"

@mcp.tool()
async def get_docs(query: str, library: str):
    """
    搜索给定查询和库的最新文档。
    支持 langchain、llama-index、autogen、agno、openai-agents-sdk、mcp-doc、camel-ai 和 crew-ai。

    参数:
    query: 要搜索的查询 (例如 "React Agent")
    library: 要搜索的库 (例如 "agno")

    返回:
    文档中的文本
    """
    if library not in docs_urls:
        raise ValueError(f"Library {library} not supported by this tool")

    query = f"site:{docs_urls[library]} {query}"
    results = await search_web(query)
    if len(results["organic"]) == 0:
        return "No results found"

    text = ""
    for result in results["organic"]:
        text += await fetch_url(result["link"])

    return text

if __name__ == "__main__":
    mcp.run(transport="stdio")

启动命令：

uv run main.py

2. 客户端配置

2.1 基于Cline

首先，在Visual Studio Code中安装Cline插件，然后配置MCP

{
  "mcpServers": {
    "mcp-server": {
      "command": "uv",
      "args": [
        "--directory",
        "<你的项目路径>",
        "run",
        "main.py"
      ]
    }
  }
}

成功绑定如下图所示（左侧绿灯亮起）： image

2.2 基于Cursor

在项目根目录创建一个.cursor文件夹，并创建一个mcp.json文件，如下图所示： image

然后将以下内容粘贴到mcp.json中

{
  "mcpServers": {
    "mcp-server": {
      "command": "uv",
      "args": [
        "--directory",
        "<你的项目路径>",
        "run",
        "main.py"
      ]
    }
  }
}

成功配置如下图所示： image

在功能中启用MCP服务 image

通过对话，通过MCP获取相关文档信息以回答问题： image

构建SSE MCP服务器（基于SSE协议）

1. 封装MCP服务器

from mcp.server.fastmcp import FastMCP
from dotenv import load_dotenv
import httpx
import json
import os
from bs4 import BeautifulSoup
from typing import Any
import httpx
from mcp.server.fastmcp import FastMCP
from starlette.applications import Starlette
from mcp.server.sse import SseServerTransport
from starlette.requests import Request
from starlette.routing import Mount, Route
from mcp.server import Server
import uvicorn

load_dotenv()

mcp = FastMCP("docs")

USER_AGENT = "docs-app/1.0"
SERPER_URL = "https://google.serper.dev/search"

docs_urls = {
    "langchain": "python.langchain.com/docs",
    "llama-index": "docs.llamaindex.ai/en/stable",
    "autogen": "microsoft.github.io/autogen/stable",
    "agno": "docs.agno.com",
    "openai-agents-sdk": "openai.github.io/openai-agents-python",
    "mcp-doc": "modelcontextprotocol.io",
    "camel-ai": "docs.camel-ai.org",
    "crew-ai": "docs.crewai.com"
}

async def search_web(query: str) -> dict | None:
    payload = json.dumps({"q": query, "num": 2})

    headers = {
        "X-API-KEY": os.getenv("SERPER_API_KEY"),
        "Content-Type": "application/json",
    }

    async with httpx.AsyncClient() as client:
        try:
            response = await client.post(
                SERPER_URL, headers=headers, data=payload, timeout=30.0
            )
            response.raise_for_status()
            return response.json()
        except httpx.TimeoutException:
            return {"organic": []}

async def fetch_url(url: str):
    async with httpx.AsyncClient() as client:
        try:
            response = await client.get(url, timeout=30.0)
            soup = BeautifulSoup(response.text, "html.parser")
            text = soup.get_text()
            return text
        except httpx.TimeoutException:
            return "Timeout error"

@mcp.tool()
async def get_docs(query: str, library: str):
    """
    搜索给定查询和库的最新文档。
    支持 langchain、llama-index、autogen、agno、openai-agents-sdk、mcp-doc、camel-ai 和 crew-ai。

    参数:
    query: 要搜索的查询 (例如 "React Agent")
    library: 要搜索的库 (例如 "agno")

    返回:
    文档中的文本
    """
    if library not in docs_urls:
        raise ValueError(f"Library {library} not supported by this tool")

    query = f"site:{docs_urls[library]} {query}"
    results = await search_web(query)
    if len(results["organic"]) == 0:
        return "No results found"

    text = ""
    for result in results["organic"]:
        text += await fetch_url(result["link"])

    return text

## sse传输
def create_starlette_app(mcp_server: Server, *, debug: bool = False) -> Starlette:
    """创建一个Starlette应用程序，可以使用提供的mcp服务器通过SSE进行服务。"""
    sse = SseServerTransport("/messages/")

    async def handle_sse(request: Request) -> None:
        async with sse.connect_sse(
                request.scope,
                request.receive,
                request._send,  # noqa: SLF001
        ) as (read_stream, write_stream):
            await mcp_server.run(
                read_stream,
                write_stream,
                mcp_server.create_initialization_options(),
            )

    return Starlette(
        debug=debug,
        routes=[
            Route("/sse", endpoint=handle_sse),
            Mount("/messages/", app=sse.handle_post_message),
        ],
    )

if __name__ == "__main__":
    mcp_server = mcp._mcp_server

    import argparse

    parser = argparse.ArgumentParser(description='运行基于SSE的MCP服务器')
    parser.add_argument('--host', default='0.0.0.0', help='要绑定的主机')
    parser.add_argument('--port', type=int, default=8020, help='监听的端口')
    args = parser.parse_args()

    # 绑定SSE请求处理到MCP服务器
    starlette_app = create_starlette_app(mcp_server, debug=True)

    uvicorn.run(starlette_app, host=args.host, port=args.port)

启动命令：

uv run main.py --host 0.0.0.0 --port 8020

上述MCP服务器代码可以直接在您的云服务器上运行。

2. 构建MCP客户端

import asyncio
import json
import os
from typing import Optional
from contextlib import AsyncExitStack
import time
from mcp import ClientSession
from mcp.client.sse import sse_client

from openai import AsyncOpenAI
from dotenv import load_dotenv

load_dotenv()  # 从.env加载环境变量

class MCPClient:
    def __init__(self):
        # 初始化会话和客户端对象
        self.session: Optional[ClientSession] = None
        self.exit_stack = AsyncExitStack()
        self.openai = AsyncOpenAI(api_key=os.getenv("OPENAI_API_KEY"), base_url=os.getenv("OPENAI_BASE_URL"))

    async def connect_to_sse_server(self, server_url: str):
        """连接到使用SSE传输的MCP服务器"""
        # 存储上下文管理器以便它们保持存活
        self._streams_context = sse_client(url=server_url)
        streams = await self._streams_context.__aenter__()

        self._session_context = ClientSession(*streams)
        self.session: ClientSession = await self._session_context.__aenter__()

        # 初始化
        await self.session.initialize()

        # 列出可用工具以验证连接
        print("初始化SSE客户端...")
        print("列出工具...")
        response = await self.session.list_tools()
        tools = response.tools
        print("\n已连接到具有以下工具的服务器：", [tool.name for tool in tools])

    async def cleanup(self):
        """正确清理会话和流"""
        if self._session_context:
            await self._session_context.__aexit__(None, None, None)
        if self._streams_context:
            await self._streams_context.__aexit__(None, None, None)

    async def process_query(self, query: str) ->