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karannsharma01

Weather MCP Server

get_forecast

Retrieve a weather forecast for any location using latitude and longitude coordinates. Provides current conditions and predictions from the National Weather Service.

Instructions

Get weather forecast for a location.

Args: latitude: Latitude of the location longitude: Longitude of the location

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latitudeYes
longitudeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main handler function for the 'get_forecast' tool. It takes latitude/longitude, calls the NWS API to get the forecast grid endpoint, fetches the forecast, and returns the next 5 periods formatted with temperature, wind, and detailed forecast.
    @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)
  • weather.py:56-56 (registration)
    The '@mcp.tool()' decorator registers the get_forecast function as an MCP tool on the FastMCP server instance.
    @mcp.tool()
  • Helper function used by get_forecast to make HTTP requests to the NWS API with error handling.
    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
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description must carry the full burden of behavioral disclosure. It only states it gets a forecast (implying a read operation) but does not mention any additional behavioral traits such as data sources, freshness, limitations, or error conditions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very short, with two sentences. It is front-loaded with the main purpose, but the 'Args:' section is slightly verbose and could be more concise. Overall, it is efficient with minimal waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with two parameters and an output schema, the description covers the basic purpose and parameter meanings. However, it lacks usage guidance and behavioral context, making it just adequate for the complexity level.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although the input schema has no descriptions (0% coverage), the description provides a brief explanation for each parameter: 'Latitude of the location' and 'Longitude of the location.' This adds meaning beyond the schema types, though it is minimal.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get weather forecast for a location.' It uses a specific verb ('get') and resource ('weather forecast'), and it implicitly distinguishes from the sibling tool 'get_alerts' by focusing on forecasts rather than alerts.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool versus the sibling 'get_alerts' or any other tool. No prerequisites or context are provided, making it difficult for an agent to decide to use this over alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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