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pragaurav44

WeatherMCP

by pragaurav44

get_forecast

Retrieve weather forecasts for specific geographic coordinates using latitude and longitude inputs to access location-based weather data.

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 handler function for the 'get_forecast' tool, decorated with @mcp.tool() for registration. It fetches the weather forecast for given latitude and longitude using the National Weather Service API, formats the next 5 periods, and returns 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)
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states the basic action but lacks critical details such as data freshness, rate limits, error conditions, authentication requirements, or response format. This is inadequate for a tool that presumably interacts with external weather data.

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 efficiently structured with a clear purpose statement followed by parameter explanations. Both sentences earn their place, though the parameter section could be more integrated rather than a separate 'Args:' block.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no annotations, no output schema, and a sibling tool, the description is incomplete. It lacks information about what the forecast returns (e.g., time periods, weather elements), how it differs from 'get_alerts', and behavioral constraints. The agent would struggle to use this effectively without additional 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?

The description lists both parameters (latitude and longitude) and explains they represent the location coordinates, adding meaning beyond the schema's 0% description coverage. However, it doesn't specify coordinate formats, valid ranges, or units, leaving gaps in parameter understanding.

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 likely serves a related but distinct weather-related function.

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 its sibling 'get_alerts', nor does it mention any prerequisites, constraints, or alternative scenarios. Usage context is implied but not explicitly stated.

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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