MCP笔记01: 快速创建一个MCP Server

MCP笔记01: 快速创建一个MCP Server

📅 2025-03-30 | 🖱️
🔖 mcp

目前许多大型语言模型(LLMs)还不具备获取天气预报和灾害天气预警的能力。让我们使用MCP来解决这个问题!

在本文中,我们将构建一个MCP Server,暴露两个工具(tools):get-alerts(获取预警)和get-forecast(获取天气预报)。

MCP核心概念 #

MCP服务器可以提供三种主要类型的能力:

  • 资源(Resources):类似文件的数据,可被客户端读取(如API响应或文件内容)
  • 工具(Tools):可被大语言模型调用的函数(需要用户批准)
  • 提示词(Prompts):预先编写的模板,帮助用户完成特定任务

创建项目 #

这里将使用MCP的Python SDK来创建一个名称为weather的MCP Server。首先创建项目:

1uv init -p 3.12 weather
2
3cd weather

创建Python虚拟机环境:

1uv venv
2source .venv/bin/activate

安装依赖:

1uv add "mcp[cli]" httpx
  • mcp是MCP的Python SDK库,使用uv引入依赖时使用uv add "mcp[cli]"
  • httpx是Python的一个现代化HTTP客户端库,它提供了同步和异步API来发送HTTP请求。它是一个很受欢迎的requests库的替代品,但具有更多现代化的功能。

开发MCP Server #

创建weather.py文件:

 1from typing import Any
 2import httpx
 3from mcp.server.fastmcp import FastMCP
 4
 5
 6mcp = FastMCP("weather")
 7
 8# Constants
 9NWS_API_BASE = "https://api.weather.gov"
10USER_AGENT = "weather-app/1.0"

FastMCP类使用Python类型提示和文档字符串来自动生成工具(tool)定义,使得创建和维护MCP tool变得更加简单。

接下来,让我们在weather.py中添加辅助函数,用于查询和格式化来自美国国家气象服务(NWS_) API的数据:

 1async def make_nws_request(url: str) -> dict[str, Any] | None:
 2    """Make a request to the NWS API with proper error handling."""
 3    headers = {"User-Agent": USER_AGENT, "Accept": "application/geo+json"}
 4    async with httpx.AsyncClient() as client:
 5        try:
 6            response = await client.get(url, headers=headers, timeout=30.0)
 7            response.raise_for_status()
 8            return response.json()
 9        except Exception:
10            return None
11
12
13def format_alert(feature: dict) -> str:
14    """Format an alert feature into a readable string."""
15    props = feature["properties"]
16    return f"""
17Event: {props.get('event', 'Unknown')}
18Area: {props.get('areaDesc', 'Unknown')}
19Severity: {props.get('severity', 'Unknown')}
20Description: {props.get('description', 'No description available')}
21Instructions: {props.get('instruction', 'No specific instructions provided')}
22"""

实现工具执行,工具执行处理器负责实际执行每个工具的逻辑。让我们添加它:

 1@mcp.tool()
 2async def get_alerts(state: str) -> str:
 3    """Get weather alerts for a US state.
 4
 5    Args:
 6        state: Two-letter US state code (e.g. CA, NY)
 7    """
 8    url = f"{NWS_API_BASE}/alerts/active/area/{state}"
 9    data = await make_nws_request(url)
10
11    if not data or "features" not in data:
12        return "Unable to fetch alerts or no alerts found."
13
14    if not data["features"]:
15        return "No active alerts for this state."
16
17    alerts = [format_alert(feature) for feature in data["features"]]
18    return "\n---\n".join(alerts)
19
20
21@mcp.tool()
22async def get_forecast(latitude: float, longitude: float) -> str:
23    """Get weather forecast for a location.
24
25    Args:
26        latitude: Latitude of the location
27        longitude: Longitude of the location
28    """
29    # First get the forecast grid endpoint
30    points_url = f"{NWS_API_BASE}/points/{latitude},{longitude}"
31    points_data = await make_nws_request(points_url)
32
33    if not points_data:
34        return "Unable to fetch forecast data for this location."
35
36    # Get the forecast URL from the points response
37    forecast_url = points_data["properties"]["forecast"]
38    forecast_data = await make_nws_request(forecast_url)
39
40    if not forecast_data:
41        return "Unable to fetch detailed forecast."
42
43    # Format the periods into a readable forecast
44    periods = forecast_data["properties"]["periods"]
45    forecasts = []
46    for period in periods[:5]:  # Only show next 5 periods
47        forecast = f"""
48{period['name']}:
49Temperature: {period['temperature']}°{period['temperatureUnit']}
50Wind: {period['windSpeed']} {period['windDirection']}
51Forecast: {period['detailedForecast']}
52"""
53        forecasts.append(forecast)
54
55    return "\n---\n".join(forecasts)

最后加上启动mcp server的代码:

1if __name__ == "__main__":
2    # Initialize and run the server
3    mcp.run(transport="stdio")

使用MCP Server #

Internet

My Computer

MCP Protocol

Web APIs

Cherry Studio
(with MCP Client)

Weather MCP Server

NWS API
Service

如上图所示,我将在Cherry Studio中使用刚刚开发的MCP Server。Cherry Studio是一款功能强大的桌面客户端,支持多种大语言模型(LLM)服务商,完美兼容Windows、macOS和Linux操作系统。由于Cherry Studio已经内置了MCP Client,我们只需在Cherry Studio中配置并启动我们刚刚开发的MCP Server,即可无缝集成这些功能。

 1{
 2  "mcpServers": {
 3    "aPS_XksxKP-paDOrI1zqP": {
 4      "name": "weather",
 5      "type": "stdio",
 6      "description": "",
 7      "isActive": true,
 8      "registryUrl": "",
 9      "command": "uv",
10      "args": [
11        "--directory",
12        "/ABSOLUTE/PATH/TO/PARENT/FOLDER/weather",
13        "run",
14        "weather.py"
15      ]
16    }
17  }
18}

注意

上述配置是Cherry Studio在添加MCP Server时自动生成的专用配置格式。其他集成了MCP Client的ChatBot、IDE(如Cursor编辑器)可能使用不同的配置文件格式,请参考相应工具的官方文档进行配置。

在Cherry Studio中需要选择支持Tool Calling的模型,这里使用的是gpt-4o,具体效果如下图所示:

参考 #

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