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get_forecast

Retrieve weather forecast data for specific coordinates using latitude and longitude parameters to access location-based meteorological information.

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 handler function for the 'get_forecast' tool. It is registered via the @mcp.tool() decorator. Retrieves the weather forecast for a given latitude and longitude by querying the National Weather Service API, parsing the response, and formatting the next 5 forecast periods into a readable 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, timeout, 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"}
        # Creates an HTTP client using httpx library (async alternative to requests)
        # async with ensures client closes properly
        async with httpx.AsyncClient() as client:
            try:
                response = await client.get(url, headers=headers, timeout=30.0)
                response.raise_for_status()  # Raises error if status code is 4xx/5xx
                return response.json()
            # If anything fails, returns None instead of crashing
            except Exception:
                return None
  • weather.py:78-78 (registration)
    The @mcp.tool() decorator registers the get_forecast function as an MCP tool, making it available for invocation by language models.
    @mcp.tool()
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 only states the basic action ('Get weather forecast') without mentioning any behavioral traits such as rate limits, authentication needs, data freshness, error handling, or what the forecast includes (e.g., temperature, precipitation). This leaves significant gaps in understanding how the tool 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, with the core purpose stated first in a clear sentence. The parameter list is concise and directly relevant. While efficient, it could be slightly improved by integrating parameter details more seamlessly, but it avoids unnecessary verbosity.

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 (2 required parameters) and the presence of an output schema, the description is minimally adequate. It covers the basic purpose and parameters but lacks behavioral context and usage guidelines. The output schema likely handles return values, so the description doesn't need to explain those, but overall completeness is limited.

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

Parameters3/5

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

The description lists the parameters (latitude and longitude) and their purpose ('of the location'), which adds some meaning beyond the input schema's basic titles and types. However, with 0% schema description coverage, it doesn't fully compensate by providing details like valid ranges, units, or examples, keeping it at the baseline for minimal parameter context.

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 with a specific verb ('Get') and resource ('weather forecast for a location'), making it immediately understandable. However, it doesn't explicitly differentiate from its sibling tool 'get_alerts', which might provide related weather information, so it doesn't reach the highest score.

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 like 'get_alerts'. It states what the tool does but offers no context about appropriate use cases, prerequisites, or exclusions, leaving the agent to infer usage from the tool name alone.

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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