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

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

# 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

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"
}

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

# 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")

uv run main.py

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

{
  "mcpServers": {
    "mcp-server": {
      "command": "uv",
      "args": [
        "--directory",
        "<你的项目路径>",
        "run",
        "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("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:
    """Create a Starlette application that can serve the provided mcp server with SSE."""
    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='Run MCP SSE-based server')
    parser.add_argument('--host', default='0.0.0.0', help='Host to bind to')
    parser.add_argument('--port', type=int, default=8020, help='Port to listen on')
    args = parser.parse_args()

    # Bind SSE request handling to MCP server
    starlette_app = create_starlette_app(mcp_server, debug=True)

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

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

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()  # load environment variables from .env

class MCPClient:
    def __init__(self):
        # Initialize session and client objects
        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):
        """Connect to an MCP server running with SSE transport"""
        # Store the context managers so they stay alive
        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__()

        # Initialize
        await self.session.initialize()

        # List available tools to verify connection
        print("Initialized SSE client...")
        print("Listing tools...")
        response = await self.session.list_tools()
        tools = response.tools
        print("\nConnected to server with tools:", [tool.name for tool in tools])

    async def cleanup(self):
        """Properly clean up the session and streams"""
        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) -> str:
        """Process a query using OpenAI API and available tools"""
        messages = [
            {
                "role": "user",
                "content": query
            }
        ]

        response = await self.session.list_tools()
        available_tools = [{ 
            "type": "function",
            "function": {
                "name": tool.name,
                "description": tool.description,
                "parameters": tool.inputSchema
            }
        } for tool in response.tools]

        # Initial OpenAI API call
        completion = await self.openai.chat.completions.create(
            model=os.getenv("OPENAI_MODEL"),
            max_tokens=1000,
            messages=messages,
            tools=available_tools
        )

        # Process response and handle tool calls
        tool_results = []
        final_text = []
        
        assistant_message = completion.choices[0].message
        
        if assistant_message.tool_calls:
            for tool_call in assistant_message.tool_calls:
                tool_name = tool_call.function.name
                tool_args = json.loads(tool_call.function.arguments)

                # Execute tool call
                result = await self.session.call_tool(tool_name, tool_args)
                tool_results.append({"call": tool_name, "result": result})
                final_text.append(f"[Calling tool {tool_name} with args {tool_args}]")

                # Continue conversation with tool results
                messages.extend([
                    {
                        "role": "assistant",
                        "content": None,
                        "tool_calls": [tool_call]
                    },
                    {
                        "role": "tool",
                        "tool_call_id": tool_call.id,
                        "content": result.content[0].text
                    }
                ])

                print(f"Tool {tool_name} returned: {result.content[0].text}")
                print("messages", messages)
                # Get next response from OpenAI
                completion = await self.openai.chat.completions.create(
                    model=os.getenv("OPENAI_MODEL"),
                    max_tokens=1000,
                    messages=messages,
                )  
                if isinstance(completion.choices[0].message.content, (dict, list)):
                    final_text.append(str(completion.choices[0].message.content))
                else:
                    final_text.append(completion.choices[0].message.content)
        else: 
            if isinstance(assistant_message.content, (dict, list)):
                final_text.append(str(assistant_message.content))
            else:
                final_text.append(assistant_message.content)

        return "\n".join(final_text)

    async def chat_loop(self):
        """Run an interactive chat loop"""
        print("\nMCP Client Started!")
        print("Type your queries or 'quit' to exit.")
        
        while True:
            try:
                query = input("\nQuery: ").strip()
                
                if query.lower() == 'quit':
                    break
                    
                response = await self.process_query(query)
                print("\n" + response)
                    
            except Exception as e:
                print(f"\nError: {str(e)}")

async def main():
    if len(sys.argv) < 2:
        print("Usage: uv run client.py <URL of SSE MCP server (i.e. http://localhost:8080/sse)>")
        sys.exit(1)

    client = MCPClient()
    try:
        await client.connect_to_sse_server(server_url=sys.argv[1])
        await client.chat_loop()
    finally:
        await client.cleanup()

if __name__ == "__main__":
    import sys
    asyncio.run(main())

uv run client.py http://0.0.0.0:8020/sse