首先编写知心天气的服务端,api key请到心知天气 - 高精度气象数据 - 天气数据API接口 - 行业气象解决方案申请,命名为server.py
import json
import httpx
from typing import Any
from mcp.server.fastmcp import FastMCP
# 初始化 MCP 服务器
mcp = FastMCP("WeatherServer")
# 知心天气 API 配置
OPENWEATHER_API_BASE = "https://api.seniverse.com/v3/weather/now.json"
API_KEY = "****" # 请替换为你自己的 知心天气 API Key
USER_AGENT = "weather-app/1.0"
async def fetch_weather(city: str) -> dict[str, Any] | None:
"""
从 知心天气 API 获取天气信息。
:param city: 城市名称(需使用英文,如 beijing)
:return: 天气数据字典;若出错返回包含 error 信息的字典
"""
params = {
"location": city,
"key": API_KEY,
"language": "zh-Hans",
"unit": "c"
}
async with httpx.AsyncClient() as client:
try:
response = await client.get(OPENWEATHER_API_BASE, params=params, timeout=30.0)
print(response)
response.raise_for_status()
return response.json() # 返回字典类型
except httpx.HTTPStatusError as e:
return {"error": f"HTTP 错误: {e.response.status_code}"}
except Exception as e:
return {"error": f"请求失败: {str(e)}"}
def format_weather(data: dict[str, Any] | str) -> str:
"""
将天气数据格式化为易读文本。
:param data: 天气数据(可以是字典或 JSON 字符串)
:return: 格式化后的天气信息字符串
"""
# 如果传入的是字符串,则先转换为字典
if isinstance(data, str):
try:
data = json.loads(data)
except Exception as e:
return f"无法解析天气数据: {e}"
# 如果数据中包含错误信息,直接返回错误提示
if "status_code" in data:
return f"⚠️ {data['error']}"
# 提取数据时做容错处理
result = data.get("results", [{}])[0] # 若results为空则返回空字典
# 解析location信息(带多层防护)
location = result.get("location", {})
city = location.get("name", "未知")
country = location.get("country", "未知")
# 解析now对象(类型转换增强)
now = result.get("now", {})
temp = now.get("temperature", "N/A") # 保持字符串类型,如需数值可做转换
description = now.get("text", "未知")
# 处理旧版接口字段(新版无对应字段)
humidity = "N/A" # 新版数据结构无湿度字段
wind_speed = "N/A" # 新版数据结构无风速字段
return (
f"🌍 {city}, {country}\n"
f"🌡 温度: {temp}°C\n"
f"💧 湿度: {humidity}%\n"
f"🌬 风速: {wind_speed} m/s\n"
f"🌤 天气: {description}\n"
)
@mcp.tool()
async def query_weather(city: str) -> str:
"""
输入指定城市的英文名称,返回今日天气查询结果。
:param city: 城市名称(需使用英文)
:return: 格式化后的天气信息
"""
data = await fetch_weather(city)
return format_weather(data)
if __name__ == "__main__":
# 以标准 I/O 方式运行 MCP 服务器
mcp.run(transport='stdio')
服务端代码 保存为 client_weather.py:
import asyncio
import os
import json
from typing import Optional
from contextlib import AsyncExitStack
from openai import OpenAI
from dotenv import load_dotenv
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
# 加载 .env 文件,确保 API Key 受到保护
load_dotenv()
class MCPClient:
def __init__(self):
"""初始化 MCP 客户端"""
self.exit_stack = AsyncExitStack()
self.openai_api_key = os.getenv("OPENAI_API_KEY") # 读取 OpenAI API Key
self.base_url = os.getenv("BASE_URL") # 读取 BASE YRL
self.model = os.getenv("MODEL") # 读取 model
if not self.openai_api_key:
raise ValueError("❌ 未找到 OpenAI API Key,请在 .env 文件中设置 OPENAI_API_KEY")
self.client = OpenAI(api_key=self.openai_api_key, base_url=self.base_url) # 创建OpenAI client
self.session: Optional[ClientSession] = None
self.exit_stack = AsyncExitStack()
async def connect_to_server(self, server_script_path: str):
"""连接到 MCP 服务器并列出可用工具"""
is_python = server_script_path.endswith('.py')
is_js = server_script_path.endswith('.js')
if not (is_python or is_js):
raise ValueError("服务器脚本必须是 .py 或 .js 文件")
command = "python" if is_python else "node"
server_params = StdioServerParameters(
command=command,
args=[server_script_path],
env=None
)
# 启动 MCP 服务器并建立通信
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()
# 列出 MCP 服务器上的工具
response = await self.session.list_tools()
tools = response.tools
print("\n已连接到服务器,支持以下工具:", [tool.name for tool in tools])
async def process_query(self, query: str) -> str:
"""
使用大模型处理查询并调用可用的 MCP 工具 (Function Calling)
"""
messages = [{"role": "user", "content": query}]
response = await self.session.list_tools()
available_tools = [{
"type": "function",
"function": {
"name": tool.name,
"description": tool.description,
"input_schema": tool.inputSchema
}
} for tool in response.tools]
response = self.client.chat.completions.create(
model=self.model,
messages=messages,
tools=available_tools
)
# 处理返回的内容
content = response.choices[0]
if content.finish_reason == "tool_calls":
# 如何是需要使用工具,就解析工具
tool_call = content.message.tool_calls[0]
tool_name = tool_call.function.name
tool_args = json.loads(tool_call.function.arguments)
# 执行工具
result = await self.session.call_tool(tool_name, tool_args)
print(result)
print(f"\n\n[Calling tool {tool_name} with args {tool_args}]\n\n")
print("#########################")
print(result.content[0].text)
# 将模型返回的调用哪个工具数据和工具执行完成后的数据都存入messages中
messages.append(content.message.model_dump())
messages.append({
"role": "tool",
"content": result.content[0].text,
"tool_call_id": tool_call.id,
})
# 将上面的结果再返回给大模型用于生产最终的结果
response = self.client.chat.completions.create(
model=self.model,
messages=messages,
)
return response.choices[0].message.content
return content.message.content
async def chat_loop(self):
"""运行交互式聊天循环"""
print("\n🤖 MCP 客户端已启动!输入 'quit' 退出")
while True:
try:
query = input("\nuser: ").strip()
if query.lower() == 'quit':
break
response = await self.process_query(query) # 发送用户输入到 OpenAI API
print(f"\n🤖 OpenAI: {response}")
except Exception as e:
print(f"\n⚠️ 发生错误: {str(e)}")
async def cleanup(self):
"""清理资源"""
await self.exit_stack.aclose()
async def main():
if len(sys.argv) < 2:
print("Usage: python client.py <path_to_server_script>")
sys.exit(1)
client = MCPClient()
try:
await client.connect_to_server(sys.argv[1])
await client.chat_loop()
finally:
await client.cleanup()
if __name__ == "__main__":
import sys
asyncio.run(main())
环境变量代码:.env 这里使用阿里百炼的免费赠送的百万token api,这里需要注意,不是所有模型都是支持mcp的,我测试过了阿里qwq-32不支持可能需要自己修改代码才能支持,本人没有去折腾它。
BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
MODEL=qwen-plus
OPENAI_API_KEY=***
一键启动
uv run client_weather.py server.py