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get_alerts

Retrieve active weather alerts for any US state using the two-letter state code to stay informed about severe conditions.

Instructions

Get weather alerts for a US state.

Args:
    state: Two-letter US state code (e.g. CA, NY)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stateYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main handler function for the 'get_alerts' tool. It fetches active weather alerts from the National Weather Service API for a given US state code, processes the data, and formats the results into a readable string.
    @mcp.tool()
    async def get_alerts(state: str) -> str:
        """Get weather alerts for a US state.
    
        Args:
            state: Two-letter US state code (e.g. CA, NY)
        """
        url = f"{NWS_API_BASE}/alerts/active/area/{state}"
        data = await make_nws_request(url)
    
        if not data or "features" not in data:
            return "Unable to fetch alerts or no alerts found."
    
        if not data["features"]:
            return "No active alerts for this state."
    
        alerts = [format_alert(feature) for feature in data["features"]]
        return "\n---\n".join(alerts)
  • Helper function to make 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"}
    
        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
  • Helper function to format a single weather alert feature into a human-readable string.
    def format_alert(feature: dict) -> str:
        """Format an alert feature into a readable string."""
        props = feature["properties"]
        return f"""
                Event: {props.get('event', 'Unknown')}
                Area: {props.get('areaDesc', 'Unknown')}
                Severity: {props.get('severity', 'Unknown')}
                Description: {props.get('description', 'No description available')}
                Instructions: {props.get('instruction', 'No specific instructions provided')}
                """
  • weather.py:38-38 (registration)
    The @mcp.tool() decorator registers the get_alerts function as an MCP tool.
    @mcp.tool()
  • The docstring provides the input schema description for the tool, specifying the 'state' parameter.
    """Get weather alerts for a US state.
    
    Args:
        state: Two-letter US state code (e.g. CA, NY)
    """
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 states the tool 'gets' data, implying a read-only operation, but doesn't specify details like rate limits, authentication needs, error handling, or what the output contains (though an output schema exists). For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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 appropriately sized and front-loaded. The first sentence clearly states the purpose, and the second sentence efficiently explains the parameter with an example. There's no wasted text, and the structure makes it easy to scan and understand.

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

Completeness4/5

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

Given the tool's low complexity (one parameter) and the presence of an output schema (which handles return values), the description is mostly complete. It covers the purpose and parameter semantics well. However, it lacks usage guidelines and behavioral details (e.g., error cases), which are minor gaps in an otherwise solid description.

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. The schema has 0% description coverage, only listing 'state' as a required string. The description compensates by explaining that 'state' is a 'Two-letter US state code (e.g. CA, NY)', providing crucial format and example details that aren't in the schema.

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 alerts for a US state.' It specifies the verb ('Get'), resource ('weather alerts'), and scope ('US state'), making it easy to understand. However, it doesn't explicitly differentiate from its sibling tool 'get_forecast' (which likely provides forecast data rather than alerts), 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. It doesn't mention the sibling tool 'get_forecast' or clarify scenarios where alerts are preferred over forecasts (e.g., for severe weather warnings). Without such context, users must 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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