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OleBo

Weather MCP Server

by OleBo

get_forecast

Retrieve weather forecasts for specific locations using latitude and longitude coordinates to plan activities and prepare for conditions.

Instructions

Get weather forecast for a location.

Args: latitude: Latitude of the location (recommended: up to 4 decimal places) longitude: Longitude of the location (recommended: up to 4 decimal places)

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 decorated with @mcp.tool() for registration and implements the logic to retrieve and format weather forecast data from the National Weather Service (NWS) API based on latitude and longitude.
        @mcp.tool()
        async def get_forecast(latitude: float, longitude: float) -> str:
            """Get weather forecast for a location.
    
            Args:
                latitude: Latitude of the location (recommended: up to 4 decimal places)
                longitude: Longitude of the location (recommended: up to 4 decimal places)
            """
            # 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 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(follow_redirects=True) 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 only states what the tool does ('Get weather forecast') without mentioning any behavioral traits like rate limits, authentication requirements, data freshness, error conditions, or what the forecast includes (e.g., temperature, precipitation). This leaves significant gaps for an agent to understand how to use it effectively.

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 in the first sentence. The parameter details are structured clearly under 'Args:' but could be slightly more concise by integrating recommendations into a single line per parameter. Overall, it's efficient with minimal waste.

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 (which handles return values), the description is partially complete. It covers the purpose and parameters well but lacks behavioral context and usage guidelines. With no annotations, it should provide more operational details to be fully helpful for an agent.

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

Parameters5/5

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

The description adds substantial meaning beyond the input schema, which has 0% description coverage. It explicitly lists both parameters (latitude and longitude) and provides practical guidance: 'recommended: up to 4 decimal places.' This clarifies precision expectations that aren't captured in the schema's basic type definitions, fully compensating for the schema's lack of descriptions.

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.' This specifies both the verb ('Get') and the resource ('weather forecast') with the target ('location'). However, it doesn't explicitly differentiate from the sibling tool 'get_alerts', which likely provides different weather-related information.

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. Usage is implied through the tool name 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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