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Active CAP weather warnings for a Spanish region

get_warnings

Get official AEMET CAP weather alerts for a Spanish autonomous community or the entire country. Filter by minimum severity and preferred language to see only relevant warnings.

Instructions

Returns the most recent CAP (Common Alerting Protocol) weather warnings issued by AEMET for the given autonomous community, or for the whole country ('esp'). Severity follows the CAP standard: Minor, Moderate, Severe, Extreme.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
areaYesSpanish autonomous community: name ('Cataluña', 'Madrid', 'La Rioja'), 2-digit AEMET code (61-78), or 'esp' for national.
languageNoPreferred warning language (default 'es'). 'any' returns the first available.
minSeverityNoOnly return warnings at or above this severity.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations present, the description carries the disclosure burden, and it provides useful context: data source (AEMET), protocol (CAP), scope options, and the severity scale. It does not clarify the output structure or reconcile the title's 'Active' with the description's 'most recent', but it is neither misleading nor tautological.

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?

Two tight sentences with no wasted words. The main action and scope are front-loaded, and the severity clarification is useful and brief.

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?

The input side is well covered by the schema, and the description explains the domain and severity model. However, with no output schema, the agent still lacks information about the return structure, empty-result behavior, and whether results are truly active or simply most recently issued.

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

Parameters3/5

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

Schema description coverage is 100%, so the parameters are already fully documented in the schema. The description reinforces the area/severity concepts but adds little beyond the existing property descriptions.

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 ('Returns'), a concrete resource ('CAP weather warnings issued by AEMET'), and a clear geographic scope (autonomous community or 'esp'). This makes the tool easy to distinguish from sibling forecast, observation, and history tools.

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?

The intended use—retrieving AEMET weather warnings for a region—is implied clearly by the description, and the unique 'warnings' resource separates it from siblings. However, it does not explicitly state when to prefer this tool over alternatives or give any exclusion criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.