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get_alerts

Retrieve weather alerts for any US state using two-letter state codes to monitor severe weather conditions and stay informed about local warnings.

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 handler function implementing the 'get_alerts' tool. It is registered via the @mcp.tool() decorator, fetches active weather alerts for the specified US state using the NWS API, handles errors, formats the alerts, and returns a formatted 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 that makes asynchronous HTTP requests to the NWS API with proper headers, timeout, and error handling. Used by the get_alerts handler.
    """Make a request to the NWS API with proper error handling."""
    headers = {"User-Agent": USER_AGENT, "Accept": "application/geo+json"}
    # Creates an HTTP client using httpx library (async alternative to requests)
    # async with ensures client closes properly
    async with httpx.AsyncClient() as client:
        try:
            response = await client.get(url, headers=headers, timeout=30.0)
            response.raise_for_status()  # Raises error if status code is 4xx/5xx
            return response.json()
        # If anything fails, returns None instead of crashing
        except Exception:
            return None
  • Helper function that formats a single weather alert feature into a human-readable string, extracting key properties like event, area, severity, etc. Used by the get_alerts handler.
    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")}
    """
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 tool retrieves data ('Get'), implying a read-only operation, but doesn't cover aspects like rate limits, error handling, authentication needs, or what the output contains. This leaves significant gaps for a tool with no annotation support.

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, starting with the core purpose followed by parameter details in a structured 'Args' section. It avoids unnecessary fluff, though the formatting could be slightly more polished for optimal readability.

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 low complexity (one parameter) and the presence of an output schema (which handles return values), the description is moderately complete. However, with no annotations and minimal behavioral context, it doesn't fully equip the agent for reliable use, especially regarding error cases or limitations.

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

Parameters4/5

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

The description adds meaningful context beyond the input schema, which has 0% description coverage. It explains that the 'state' parameter is a 'Two-letter US state code' with examples (e.g., CA, NY), clarifying the expected format. Since there's only one parameter, this compensates well for the schema's lack of details.

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 alerts for a US state'), making it immediately understandable. However, it doesn't explicitly differentiate from its sibling tool 'get_forecast', which likely provides different weather data, 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 its sibling 'get_forecast' or any alternatives. It mentions the scope ('US state') but lacks explicit instructions on usage context, exclusions, or prerequisites, leaving the agent to infer based on tool names 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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