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aitiwari

Weather MCP Server

by aitiwari

get_alerts

Fetch real-time weather alerts for any US state using two-letter state codes. Access official National Weather Service warnings for severe weather 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

Implementation Reference

  • Main implementation of the get_alerts tool. Fetches weather alerts from the NWS API for a given US state code, processes the response, and formats alerts into readable strings.
    @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)
  • weather.py:44-44 (registration)
    The @mcp.tool() decorator registers get_alerts as an MCP tool with the FastMCP server. The schema is auto-generated from the function signature and docstring.
    @mcp.tool()
  • Helper function that formats an individual alert feature from the NWS API into a human-readable string with event type, area, severity, description, and instructions.
    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')}
    """
  • Helper function that makes HTTP requests to the NWS API with proper headers and error handling. Used by get_alerts to fetch alert data.
    #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
Behavior2/5

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. While 'Get' implies read-only access, the description omits whether the data is real-time or cached, what types of alerts are returned (severe weather, advisories, etc.), and the response format structure.

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 optimally concise at two sentences with no wasted words. The purpose is front-loaded in the first sentence, followed by the Args section that precisely documents the single parameter.

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?

For a single-parameter tool without output schema or annotations, the description adequately covers the essential input requirements. However, it could improve by mentioning the return value structure (e.g., list of active alerts) and distinguishing its use case from 'get_forecast' to achieve higher completeness.

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?

With 0% schema description coverage (the 'state' property has no description field), the description provides crucial format specification that the schema lacks: it clarifies the two-letter code requirement and provides examples (CA, NY). This significantly aids correct parameter construction.

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 verb (Get), resource (weather alerts), and scope (US state). However, it does not explicitly differentiate from sibling tool 'get_forecast', which could help clarify when to use alerts versus forecasts.

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, nor does it mention any prerequisites such as API keys or location validation. No 'when-not' or exclusion criteria are provided.

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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