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

构建工具函数

为了让大模型能访问市面上主流框架的技术文档，我们主要通过用户输入的 query，结合指定 site 特定域名的谷歌搜索进行搜索相关网页，并对相关网页进行解析提取网页文本并返回。

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 Server (基于 Stdio 协议)

1. MCP Server (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 安装 Cline 插件，然后进行配置 MCP

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

成功绑定如图（左侧绿灯）：

2.2 基于 Cursor

项目根目录创建 .cursor 文件夹，并创建 mcp.json 文件，如：

然后粘贴以下内容到 mcp.json

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

成功配置如图：

在 Features 开启 MCP 服务

通过对话它便通过 MCP 获取相关文档信息进行回答：

构建 SSE MCP Server (基于 SSE 协议)

1. 封装 MCP Server

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: S