从零开始实现大模型客户端开发（MCP）

1. 环境配置

pip install anthropic python-dotenv
touch .env
.env中配置ANTHROPIC_API_KEY=

2. 程序准备

import asyncio
from typing import Optional
from contextlib import AsyncExitStack
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
from anthropic import Anthropic
from dotenv import load_dotenv

load_dotenv()  # load environment variables from .env

class MCPClient:
    def __init__(self):
        self.session: Optional[ClientSession] = None
        self.exit_stack = AsyncExitStack()
        self.anthropic = Anthropic()

    async def connect_to_server(self, server_script_path: str):
        command = "python"
        server_params = StdioServerParameters(
            command=command,
            args=[server_script_path],
            env=None
        )
        stdio_transport = await self.exit_stack.enter_async_context(stdio_client(server_params))
        self.stdio, self.write = stdio_transport
        self.session = await self.exit_stack.enter_async_context(ClientSession(self.stdio, self.write))
        await self.session.initialize()
        response = await self.session.list_tools()
        tools = response.tools
        print("\n连接到MCP服务器,可用工具:", [tool.name for tool in tools])

    async def process_query(self, query: str) -> str:
        messages = [{
                "role": "user",
                "content": query
            }]
        response = await self.session.list_tools()
        available_tools = [{
            "name": tool.name,
            "description": tool.description,
            "input_schema": tool.inputSchema
        } for tool in response.tools]
        response = self.anthropic.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=1000,
            messages=messages,
            tools=available_tools
        )
        final_text = []
        assistant_message_content = []
        for content in response.content:
            if content.type == 'text':
                final_text.append(content.text)
                assistant_message_content.append(content)
            elif content.type == 'tool_use':
                tool_name = content.name
                tool_args = content.input
                result = await self.session.call_tool(tool_name, tool_args)
                final_text.append(f"[Calling tool {tool_name} with args {tool_args}]")

                assistant_message_content.append(content)
                messages.append({
                    "role": "assistant",
                    "content": assistant_message_content
                })
                messages.append({
                    "role": "user",
                    "content": [{
                            "type": "tool_result",
                            "tool_use_id": content.id,
                            "content": result.content
                    }]
                })
                response = self.anthropic.messages.create(
                    model="claude-3-5-sonnet-20241022",
                    max_tokens=1000,
                    messages=messages,
                    tools=available_tools
                )
                final_text.append(response.content[0].text)
        return "\n".join(final_text)

    async def chat_loop(self):
        while True:
            try:
                query = input("\n输入指令: ").strip()
                response = await self.process_query(query)
                print("\n" + response)
            except Exception as e:
                print(f"\n错误: {str(e)}")

    async def cleanup(self):
        await self.exit_stack.aclose()

async def main():   
    client = MCPClient()
    try:
        await client.connect_to_server(sys.argv[1])
        await client.chat_loop()
    finally:
        await client.cleanup()

#print("启动：python client.py compareServer.py")
if __name__ == "__main__":
    import sys
    asyncio.run(main())

3. 使用测试

因为某些原因没有ANTHROPIC_API_KEY，未能调用指令。

参考：https://modelcontextprotocol.io/quickstart/client