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get_forecast

Retrieve weather forecast data for any location using latitude and longitude coordinates. This tool provides detailed weather predictions to help plan activities and prepare for conditions.

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 is decorated with @mcp.tool() which handles both registration and schema inference from the function signature and docstring. Fetches the weather forecast from the National Weather Service API using the provided latitude and longitude, formats the next 5 forecast periods, and returns them as a formatted string.
    @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)
  • Helper function used by get_forecast to make asynchronous HTTP requests to the NWS API with proper headers and 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it indicates this is a read operation ('Get'), it doesn't describe any behavioral traits such as rate limits, authentication requirements, data freshness, error conditions, or what the forecast includes (e.g., temperature, precipitation). For a tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.

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 appropriately sized and front-loaded: the first sentence clearly states the purpose, followed by a structured parameter list. There's no wasted text, and every sentence serves a purpose. It could be slightly more concise by integrating the parameter descriptions more seamlessly, but overall it's efficient.

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 that there's an output schema (which handles return values), the description doesn't need to explain outputs. However, with zero annotation coverage and a sibling tool, the description is minimal. It covers the basic purpose and parameters but lacks context on usage, behavioral traits, or differentiation from alternatives. For a simple 2-parameter tool with output schema, this is adequate but has clear gaps.

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 explicitly lists both parameters (latitude and longitude) and provides basic semantic context ('Latitude of the location', 'Longitude of the location'). Since schema description coverage is 0%, the description compensates by adding meaning beyond the bare schema. However, it doesn't specify format details like coordinate ranges or units, keeping it from a perfect score.

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.' It uses a specific verb ('Get') and resource ('weather forecast'), making the function immediately understandable. However, it doesn't explicitly differentiate from its sibling tool 'get_alerts', which likely serves a related but distinct purpose in weather data retrieval.

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 no guidance on when to use this tool versus alternatives. There's no mention of the sibling tool 'get_alerts', nor any context about when a forecast is appropriate versus alerts. The description simply states what the tool does without offering usage context or exclusions.

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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