了解MCP能力以及部署到Anaconda

def  get_weather ( location: str ) -> dict :
     """取得指定位置的目前天氣。""" 
    # 連接到天氣 API 並取得資料
    return {
         "temporary" : 72 ,
         "conditions" : "Sunny" ,
         "humidity" : 45 
    }

def  read_file ( file_path: str ) -> str :
     """讀取指定路徑下檔案的內容。""" 
    with  open (file_path, 'r' ) as f:
         return f.read()

def code_review(code: str, language: str) -> list:
    """Generate a code review for the provided code snippet."""
    return [
        {
            "role": "system",
            "content": f"You are a code reviewer examining {language} code. Provide a detailed review highlighting best practices, potential issues, and suggestions for improvement."
        },
        {
            "role": "user",
            "content": f"Please review this {language} code:\\n\\n```{language}\\n{code}\\n```"
        }
    ]

def request_sampling(messages, system_prompt=None, include_context="none"):
    """Request LLM sampling from the client."""
    # In a real implementation, this would send a request to the client
    return {
        "role": "assistant",
        "content": "Analysis of the provided data..."
    }

conda activate <your_env_name>

pip install uv

uv --version

uv init
uv venv
source .venv/bin/activate
// or comment below
// .venv/bin/activate

uv pip install "mcp[cli]"

from mcp.server.fastmcp import FastMCP

# Create an MCP server
mcp = FastMCP("Weather Service")

@mcp.tool()
def get_weather(location: str) -> str:
    """Get the current weather for a specified location."""
    return f"Weather in {location}: Sunny, 72°F"

@mcp.resource("weather://{location}")
def weather_resource(location: str) -> str:
    """Provide weather data as a resource."""
    return f"Weather data for {location}: Sunny, 72°F"

@mcp.prompt()
def weather_report(location: str) -> str:
    """Create a weather report prompt."""
    return f"""You are a weather reporter. Weather report for {location}?"""

# Run the server
if __name__ == "__main__":
    mcp.run()

mcp dev your_py_file.py