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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), and the description adds real behavioral context beyond them: use is gated on an aviation sector license, the data is deterministic with no model in the loop, and it reflects the latest monthly VRA file. Remaining gaps are minor — no detail on the result shape or pagination, and openWorldHint is left unelaborated.

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 compact sentences: what is measured and from where, what question it answers, then access and determinism caveats. Front-loaded and free of filler; every clause carries information an agent would otherwise have to guess.

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?

With no output schema, the description carries the return-value burden and does so by enumerating the metrics returned (flights, cancellations, 15-minute-plus delays, rates) plus the data vintage. The main missing piece is whether omitting 'entity' returns all airlines versus a default subset.

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% and the single optional 'entity' parameter is fully documented in the schema, so the schema is doing the parameter work. The description's 'per airline' framing implies the filtering behavior but adds no syntax, matching, or fallback guidance (e.g. what happens if the name does not match).

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 names the specific resource and its provenance: per-airline scheduled flights, cancellations, and departures more than 15 minutes late from ANAC's latest monthly VRA file. It does not explicitly contrast itself with siblings such as base_rates or company_financials, but the metric set is distinctive enough that an agent can route to it from subject matter alone.

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?

It gives a concrete usage trigger — ranking or comparing Brazilian airlines by cancellations/delays — which is exactly the kind of context an agent needs. It stops short of stating when NOT to use it or naming an alternative for adjacent questions (e.g. broader financial data), so it falls short of a 5.

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