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get_forecast

Retrieve weather forecast data for specific coordinates using Slim MCP's lightweight API service, enabling AI agents and automated workflows to access location-based meteorological information.

Instructions

Get weather forecast for a location.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latitudeYes
longitudeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The core handler function implementing the get_forecast tool. It queries the National Weather Service API using provided latitude and longitude to retrieve and format the weather forecast for the next 5 periods.
    async def get_forecast(latitude: float, longitude: float) -> str:
        """Get weather forecast for a 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)
  • The registration function that uses mcp.tool() decorator to register the get_forecast tool (along with get_alerts) with the MCP server.
    def register_weather_tools(mcp):
        """Register all weather tools with the MCP server."""
        mcp.tool()(get_alerts)
        mcp.tool()(get_forecast)
  • Key helper utility function used by get_forecast to make 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:
                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 action ('Get weather forecast') but doesn't add any context about traits like rate limits, data freshness, error handling, or authentication needs, leaving 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.

Conciseness5/5

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

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.

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, no annotations, but has an output schema), the description is minimally adequate. It covers the basic purpose but lacks details on usage, behavior, and parameter context, though the output schema may help mitigate some gaps in return value explanation.

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 schema description coverage is 0%, so the description must compensate, but it only vaguely implies parameters ('for a location') without detailing the required latitude and longitude. This adds minimal meaning beyond the schema, resulting in a baseline score due to the schema's clear structure but incomplete documentation.

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 distinguish this tool from its sibling 'get_alerts', which might also provide weather-related information, so it misses full differentiation.

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' or other siblings. It lacks context about scenarios where a forecast is preferred over current conditions or alerts, offering minimal usage 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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