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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and a closed world, so the safety profile is covered. The description adds that the comparison is against FIPE and active same-model listings, but says nothing about return shape or freshness of the comparison data.

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?

Two tight sentences: the scope is front-loaded ('Posição de preço DESTE anúncio'), followed by the use-case triggers. No filler.

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 single-parameter, read-only tool with no output schema, the description conveys what the result represents (position vs FIPE and vs market). Adequate, with only minor gaps around return format and data currency.

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 description coverage is 0% for the single car_id parameter, but the parameter is a self-evident integer identifier and the description's 'DESTE anúncio' implies it selects the listing. Marginal added meaning over the schema.

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 specific resource – the price position of THIS listing – and scopes it to two benchmarks (FIPE table and active listings of the same model). This clearly separates it from siblings like get_fipe_valuation (raw valuation) and get_price_distribution, though it does not name those siblings explicitly.

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?

Gives concrete user-intent triggers ('esse preço é bom?' / 'está caro ou barato?'), which is strong when-to-use guidance. It stops short of naming when NOT to use it or pointing to an alternative tool for related questions.

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