List active NWS weather alerts (warnings, watches, advisories) for a US state or marine area. Pass a two-letter state code like CA, TX, FL. Returns event type, severity, headline, affected area and expiry.
| Name | Required | Description | Default |
|---|---|---|---|
| area | Yes | Two-letter US state/territory code, e.g. CA, TX, FL, PR. |
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of disclosing behavior. It states this is a listing/read operation, restricts scope to active alerts, and lists the returned fields. Minor ambiguity remains around the 'marine area' mention conflicting with the two-letter state code instruction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences, each adding value: the tool's purpose, the required input format, and the expected return fields. No filler or redundant wording; the most important information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter read-only tool with no output schema, the description provides enough to invoke it correctly and understand the response shape. The only real gap is the unresolved 'marine area' usage, which could mislead an agent into passing a marine zone identifier rather than a state code.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already documents the 'area' parameter as a two-letter US state/territory code. The description adds useful examples (CA, TX, FL) and mentions marine areas, but does not substantially go beyond the schema or clarify the marine-area format.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb and resource: 'List active NWS weather alerts' for a US state or marine area. It also enumerates what the alerts include (warnings, watches, advisories) and what the response contains, making it easily distinguishable from siblings like weather_current and weather_forecast.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context for when to use the tool: to retrieve active NWS alerts. It provides concrete input examples and specifies the geographic scope, but it does not explicitly contrast this tool with sibling tools such as weather_forecast or weather_current.
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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Each tool targets a distinct query type: seismic events, elevation, geocoding, weather alerts, current observations, and forecasts. There is no meaningful overlap, and the weather_current/weather_forecast/weather_alerts trio is clearly separated by description.
Tool names are lowercase and simple, with a clear weather_ prefix grouping the NWS-related tools. The main minor deviation is geocode being verb-style while earthquakes and elevation are noun-style, but the overall pattern remains predictable.
Six tools is a well-scoped size for a weather and geo intelligence server. Each tool earns its place and together they cover the major natural query categories without unnecessary overlap or bloat.
The server supports the core workflow of taking a text location, geocoding it, and then getting elevation, current weather, forecast, or earthquake data. Minor gaps like missing reverse geocoding or location-based alert search are workable but not critical.