前言
- 不是sse的，用stdio实现的
- 代码可以参照官方页面，改成了通义千问方式，而且接口返回的字段好像也和文档不一样了：modelcontextprotocol.io/quickstart/…
- 环境问题直接交给ai解决。注意先参照上面示例搭一下基础环境，python，uv，三方包先装完。
server
from typing import Any
import httpx
from mcp.server.fastmcp import FastMCP
# Initialize FastMCP server
mcp = FastMCP("weather")
# Constants
NWS_API_BASE = "https://api.weather.gov"
USER_AGENT = "weather-app/1.0"
async def make_nws_request(url: str) -> dict[str, Any] | None:
"""Make a request to the NWS API with proper error handling."""
headers = {
"User-Agent": USER_AGENT,
"Accept": "application/geo+json"
}
async with httpx.AsyncClient() as client:
try:
response = await client.get(url, headers=headers, timeout=30.0)
response.raise_for_status()
return response.json()
except Exception:
return None
def format_alert(feature: dict) -> str:
"""Format an alert feature into a readable string."""
props = feature["properties"]
return f"""
Event: {props.get('event', 'Unknown')}
Area: {props.get('areaDesc', 'Unknown')}
Severity: {props.get('severity', 'Unknown')}
Description: {props.get('description', 'No description available')}
Instructions: {props.get('instruction', 'No specific instructions provided')}
"""
@mcp.tool()
async def get_alerts(state: str) -> str:
"""Get weather alerts for a US state.
Args:
state: Two-letter US state code (e.g. CA, NY)
"""
url = f"{NWS_API_BASE}/alerts/active/area/{state}"
data = await make_nws_request(url)
if not data or "features" not in data:
return "Unable to fetch alerts or no alerts found."
if not data["features"]:
return "No active alerts for this state."
alerts = [format_alert(feature) for feature in data["features"]]
return "\n---\n".join(alerts)
@mcp.tool()
async def get_forecast(latitude: float, longitude: float) -> str:
"""Get weather forecast for a location.
Args:
latitude: Latitude of the location
longitude: Longitude of the location
"""
# First get the forecast grid endpoint
points_url = f"{NWS_API_BASE}/points/{latitude},{longitude}"
points_data = await make_nws_request(points_url)
if not points_data:
return "Unable to fetch forecast data for this location."
# Get the forecast URL from the points response
forecast_url = points_data["properties"]["forecast"]
forecast_data = await make_nws_request(forecast_url)
if not forecast_data:
return "Unable to fetch detailed forecast."
# Format the periods into a readable forecast
periods = forecast_data["properties"]["periods"]
forecasts = []
for period in periods[:5]: # Only show next 5 periods
forecast = f"""
{period['name']}:
Temperature: {period['temperature']}°{period['temperatureUnit']}
Wind: {period['windSpeed']} {period['windDirection']}
Forecast: {period['detailedForecast']}
"""
forecasts.append(forecast)
return "\n---\n".join(forecasts)
if __name__ == "__main__":
# Initialize and run the server
mcp.run(transport='stdio')
client
import asyncio
import json
import os
from typing import Optional
from contextlib import AsyncExitStack
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_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("DASHSCOPE_API_KEY"),
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
)
async def connect_to_server(self, server_script_path: str):
"""Connect to an MCP server
Args:
server_script_path: Path to the server script (.py or .js)
"""
is_python = server_script_path.endswith('.py')
is_js = server_script_path.endswith('.js')
if not (is_python or is_js):
raise ValueError("Server script must be a .py or .js file")
command = "python" if is_python else "node"
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()
# List available tools
response = await self.session.list_tools()
tools = response.tools
print("\nConnected to server with tools:", [tool.name for tool in tools])
async def process_query(self, query: str) -> str:
"""Process a query using Claude 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]
print("=========list_tools===========")
# Initial Claude API call
response = await self.openai.chat.completions.create(
model="qwen-max",
max_tokens=1000,
messages=messages,
tools=available_tools
)
print("response:", response)
# Process response and handle tool calls
tool_results = []
final_text = []
assistant_message = response.choices[0].message
cnt = 0
if assistant_message.tool_calls:
for tool_call in assistant_message.tool_calls:
print(f">>>>>>>>>>>>>>loop{cnt}")
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="qwen-max",
max_tokens=1000,
messages=messages,
)
print("completion", completion)
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 cleanup(self):
"""Clean up resources"""
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())
运行方式
uv run .\client.py .\server.py 遇到个问题找不到openai，pip已经安装了还是找不到，可以执行uv pip install openai
PS D:\project\mcp\src\test> uv run .\client.py .\server.py
Using CPython 3.12.5 interpreter at: C:\Users\Admin\AppData\Local\Programs\Python\Python312\python.exe
Creating virtual environment at: D:\project\mcp\.venv
Built mcp @ file:///D:/project/mcp
░░░░░░░░░░░░░░░░░░░░ [0/27] Installing wheels... warning: Failed to hardlink files; falling back to full copy. This may lead to degraded performance.
If the cache and target directories are on different filesystems, hardlinking may not be supported.
If this is intentional, set `export UV_LINK_MODE=copy` or use `--link-mode=copy` to suppress this warning.
Installed 27 packages in 271ms
Traceback (most recent call last):
File "D:\project\mcp\src\test\client.py", line 9, in <module>
from openai import AsyncOpenAI
ModuleNotFoundError: No module named 'openai'
PS D:\project\mcp\src\test> uv pip install openai
输入语句：what are the weather alerts in california 测试
调用tool，会输出很长一串
然后封装到message里面交给llm
等一会llm就会返回
