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aitiwari

Weather MCP Server

by aitiwari

get_forecast

Fetch detailed weather forecasts for specific geographic locations using latitude and longitude coordinates. Retrieves prediction data from the National Weather Service for accurate local weather 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

Implementation Reference

  • The main get_forecast tool implementation that fetches weather forecast from NWS API for a given latitude/longitude and returns formatted forecast data
    @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:63-63 (registration)
    MCP tool decorator that registers get_forecast as an MCP tool with the FastMCP server
    @mcp.tool()
  • Helper function make_nws_request used by get_forecast to make HTTP requests to the NWS API with proper error handling
    #helper function
    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
  • Input schema definition via type hints (latitude: float, longitude: float) and docstring Args section describing the expected parameters
    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
        """
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. It fails to specify the forecast timeframe (e.g., 5-day, hourly), units (metric/imperial), data freshness, or what the return value contains. 'Get weather forecast' is too generic for an agent to predict the operation's scope or output format.

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 with the purpose stated immediately in the first sentence. The Args section is structured and necessary given the empty schema descriptions. No extraneous text is present, though the parameter descriptions are minimal.

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 low complexity (2 primitive parameters, no nested objects) and lack of output schema, the description meets minimum viability but has clear gaps. It omits the forecast time range, output data structure, and units—information essential for an agent to utilize the results effectively. With no annotations to clarify behavior, the description should provide more operational context.

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?

With 0% schema description coverage, the description must compensate for the lack of parameter documentation. The Args block provides basic semantic mapping ('Latitude of the location'), which is minimally sufficient but tautological. It lacks coordinate format details, valid ranges, or examples that would help an agent construct valid inputs.

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 states a clear specific verb ('Get') and resource ('weather forecast') with scope ('for a location'). However, it does not explicitly differentiate from the sibling tool 'get_alerts' (forecast vs. alerts), which would help an agent select the correct tool when both weather conditions and alerts are relevant.

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 the sibling 'get_alerts' or other alternatives. There are no prerequisites, conditions, or exclusion criteria mentioned.

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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