MCP (Model Context Protocol)：AI Agent 互操作的新标准

from mcp.server import Server
from mcp.types import Tool, TextResource
from pydantic import AnyUrl
import asyncio

app = Server("my-mcp-server")

@app.list_tools()
async def list_tools() -> list[Tool]:
    return [
        Tool(
            name="get_weather",
            description="获取指定城市的天气信息",
            inputSchema={
                "type": "object",
                "properties": {
                    "city": {"type": "string", "description": "城市名称"}
                },
                "required": ["city"]
            }
        )
    ]

@app.call_tool()
async def call_tool(name: str, arguments: dict) -> str:
    if name == "get_weather":
        city = arguments["city"]
        return f"{city} 当前天气晴朗，温度 22°C"
    raise ValueError(f"未知工具: {name}")

if __name__ == "__main__":
    import mcp.server.stdio
    asyncio.run(mcp.server.stdio.run_server(app))

import { Client } from "@modelcontextprotocol/sdk/client";
import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio";

async function main() {
  const transport = new StdioClientTransport({
    command: "python",
    args: ["/path/to/mcp-server.py"]
  });
  
  const client = new Client(
    { name: "my-ai-app", version: "1.0.0" },
    { capabilities: {} }
  );
  
  await client.connect(transport);
  
  const result = await client.callTool({
    name: "get_weather",
    arguments: { city: "北京" }
  });
  
  console.log(result.content[0].text);
}

main();