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Weather MCP Server

get_alerts

Retrieve active weather alerts for any US state by providing a two-letter state code.

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 takes a two-letter US state code, calls the NWS API to fetch active alerts, and returns them formatted as a string. Decorated with @mcp.tool() to register it as an 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:36-36 (registration)
    The @mcp.tool() decorator that registers get_alerts as an MCP tool on the FastMCP server instance.
    @mcp.tool()
  • Helper function that makes HTTP requests to the NWS API with proper error handling, used by get_alerts to fetch alert data.
    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 that formats a single alert feature from the NWS API into a human-readable string, used by get_alerts for formatting output.
    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")}
    """

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. 'Get' implies a read-only operation, but the description does not explicitly state side-effect-free behavior or any caveats about alert types or data source. It is not misleading, but it adds minimal behavioral context beyond what the name implies.

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 extremely concise, with a front-loaded purpose statement followed by a compact Args block. Every sentence earns its place and there is no filler.

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

Completeness5/5

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

With a single well-documented parameter, an output schema, and no siblings, the description plus schema fully covers what an agent needs to invoke the tool correctly. No missing context for this simple operation.

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?

Schema description coverage is 0%, yet the description fully compensates by specifying the parameter format ('Two-letter US state code') and providing concrete examples ('CA, NY'). This adds real meaning beyond the raw schema type string.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Get'), a clear resource ('weather alerts'), and a clear scope ('US state'). It is unambiguous and leaves no doubt about what the tool does, even without siblings to differentiate from.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There are no sibling tools, so explicit routing guidance is unnecessary. The description clearly implies usage: when you need weather alerts for a US state. It lacks explicit exclusions, but nothing is misleading or missing for a tool of this simplicity.

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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