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tranducthai

MCP Weather SSE Server

by tranducthai

get_forecast

Retrieve weather forecasts for US locations by providing latitude and longitude coordinates.

Instructions

Get weather forecast for a location using NWS (US only).

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 execution logic for the 'get_forecast' tool. This async function is decorated with @mcp.tool(), which also serves as registration. It fetches the forecast grid point data from NWS API, retrieves the detailed forecast, and formats the next 5 periods into a string response.
    @mcp.tool()
    async def get_forecast(latitude: float, longitude: float) -> str:
        """Get weather forecast for a location using NWS (US only).
    
        Args:
            latitude: Latitude of the location
            longitude: Longitude of the location
        """
        print(f"get_forecast called with lat: {latitude}, lon: {longitude}", file=sys.stderr)
        # 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)
  • Input/output schema defined by function signature (latitude: float, longitude: float) -> str and docstring describing parameters.
    async def get_forecast(latitude: float, longitude: float) -> str:
        """Get weather forecast for a location using NWS (US only).
    
        Args:
            latitude: Latitude of the location
            longitude: Longitude of the location
        """
  • Supporting utility function used by get_forecast to make authenticated HTTP requests to the NWS API endpoints.
    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 as e:
                print(f"NWS API error: {e}", file=sys.stderr)
                return None
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool uses NWS and is limited to the US, but lacks details on rate limits, error handling, authentication needs, or what the forecast includes (e.g., time periods, data format). This leaves significant gaps for a tool that likely involves external API calls.

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 concise and well-structured: a clear purpose statement followed by a brief parameter list. Every sentence adds value, with no redundant information. However, it could be slightly more front-loaded by integrating the parameter semantics into the main description for better flow.

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?

Given the tool's moderate complexity (external API, geographic constraints), no annotations, and an output schema (which reduces the need to describe return values), the description is minimally adequate. It covers the basic purpose and parameters but misses behavioral details like rate limits or error cases, making it incomplete for robust agent use.

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?

The description adds meaningful context beyond the input schema, which has 0% description coverage. It specifies that latitude and longitude are for a 'location' and implies they must be within the US, but doesn't detail valid ranges or coordinate systems. Since there are only 2 parameters and the schema lacks descriptions, this partial compensation earns a 4, though not a 5 due to missing precision.

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

Purpose4/5

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 using NWS (US only).' It specifies the verb ('Get'), resource ('weather forecast'), and scope ('US only'), but doesn't explicitly distinguish it from sibling tools like 'get_weather_forecast' or 'get_current_weather', which limits it to a 4 rather than a 5.

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?

The description provides minimal usage guidance: it mentions 'US only' as a geographic constraint, but offers no explicit advice on when to use this tool versus alternatives like 'get_current_weather' or 'get_weather_forecast'. There's no mention of prerequisites, exclusions, or comparative contexts, leaving the agent with little direction.

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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