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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the description's job is marginal context — and it delivers: each make row carries car count, company count, and lowest published daily rate, and unknown make/state/city returns an empty list with did_you_mean rather than an error. This empty-result/fuzzy-suggestion behavior is the key non-obvious trait and is disclosed.

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?

Front-loaded with the primary capability, then usage, then edge-case behavior — a sensible order with no filler sentences. It is a single dense paragraph, slightly long, but every clause carries information an agent needs.

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?

An output schema exists, so return values need not be explained, yet the description still summarizes them; combined with the empty-list/did_you_mean note and zero required parameters, an agent has everything needed to call this correctly on the first try.

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 baseline is 3, but the description adds meaning beyond it: omitting state/city yields nationwide results, and supplying make switches the output shape from makes to that make's model families (Huracán, Urus, Aventador). The state/city/make interplay is clearer in the description than in the schema alone.

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?

States a concrete verb+resource (list car makes, and each make's model families) and scopes it precisely (nationwide, state, or city) with the aggregations returned. It is clearly distinguishable from search_vehicles, browse_locations, and the company-oriented siblings.

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?

Explicitly names the triggering intents ("which brands or models they can rent somewhere") and the alternative workflow ("check how the catalog spells a make or model before calling search_vehicles"). That is a real when-to-use and when-to-use-something-else rule, not an inference.

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