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

Annotations declare readOnlyHint=true, and the description adds substantial context: data source (Open-Meteo US-AQI), methodology (z-scored against history since 1990), update frequency (daily), and the nature of returned data (current score, percentile, trend history, source lineage). This goes well beyond the annotations and gives the agent confidence about behavior and data provenance.

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?

The description is compact and front-loaded with the core functionality in the first sentence, followed by usage guidance and update frequency. Every sentence earns its place, with no fluff or repetition. The structure flows logically from what it does, to when to use it, to operational cadence.

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 tool with one optional parameter and no output schema, the description covers all essential aspects: what it returns, data source, methodology, update frequency, and usage scenarios. It also mentions the tier requirement for history, though that is in the schema. There is no notable missing context for an agent to use it correctly.

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?

The sole parameter 'days' is fully documented in the schema (range, meaning, tier requirement), giving 100% schema coverage. The description does not mention the parameter, so it adds no incremental semantic value. Baseline of 3 applies because the schema does the heavy lifting without needing description compensation.

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 the tool returns a 0-100 composite air-quality index for major US metros, with specific outputs like current score, percentile, trend history, and source lineage. The verb 'returns' and resource 'composite air-quality index' are precise, and the title reinforces the scope. This distinguishes it from siblings that likely cover other environmental or health topics.

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 explicitly gives usage contexts: 'Call when the user asks about air pollution, smog, AQI, PM2.5, wildfire smoke, or urban environmental health, or when timing quality-of-life adjustments.' This provides strong when-to-use guidance. However, it stops short of naming alternatives or explicit when-not-to-use cases, though the sibling list includes a potentially related tool (adw.air_quality_risk).

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