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Weather Alerts

weather_alerts
Read-only

Active US National Weather Service alerts for a state or a point: event, severity, urgency, area, onset, end of the event (ends), expiry of this message (expires), headline and instruction (cut to 400 characters). Attribution: National Weather Service, public domain. Price: $0.002 in USDC per successful call, paid with x402 (Base, Solana or Arc); failed calls are not charged.

Input Schema

NameRequiredDescriptionDefault
latNoLatitude, with lon, instead of state
lonNoLongitude
stateNoTwo-letter US state, e.g. CA

Output Schema

NameRequiredDescriptionDefault
countNo
queryNo
alertsNo
sourceNo
checkedAtNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.3/5.0
Behavior5/5

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

Goes well beyond the readOnlyHint/openWorldHint annotations: discloses that instruction text is truncated at 400 characters, clarifies the distinction between 'ends' (end of the event) and 'expires' (expiry of the message), states attribution/public-domain status, and — critically — documents the x402 payment model ($0.002 USDC per successful call, no charge on failure) across Base, Solana, or Arc. The payment requirement is behavioral context an agent cannot get from any structured field.

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?

One dense sentence front-loads the resource and scope, then the field list, then the commercial terms. It is longer than minimal but every clause carries operational weight (truncation limit, field meanings, price); the attribution/price clauses could be split for readability but nothing is wasted.

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?

With annotations, a 100%-covered schema, and an output schema that carries return structure, the description covers purpose, scope, truncation, and payment. The remaining gap is operational edge-case guidance — behavior when no alerts are active, or when both state and lat/lon are given — which is minor.

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?

Schema coverage is 100%, so the baseline is 3, but the description adds the key constraint the schema cannot convey: since all three params are optional, 'for a state or a point' signals that at least one location form must be supplied, and 'a state or a point' frames lat/lon as the alternative addressing mode.

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?

States a specific resource and scope: 'Active US National Weather Service alerts for a state or a point,' and enumerates the payload fields (event, severity, urgency, area, onset, ends, expires, headline, instruction). An agent immediately knows this is active NWS alert data, not a forecast, distinguishing it from the sibling `weather` tool by scope and source.

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

Usage Guidelines3/5

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

Usage is implied by 'for a state or a point,' which tells the agent the two supported query modes, but there is no explicit when-to-use vs. when-not, no mention of when to prefer `weather` for forecasts, and no guidance on choosing state vs. lat/lon granularity.

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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