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

The annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description adds useful behavioral context by naming Mercado Libre as the price-guide source and describing the aggregated output (median, range, opportunities, cheapest). It does not disclose limitations like data freshness, currency, or how 'opportunities' are defined, so it is adequate but not richly transparent.

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 two purposeful sentences with the core question front-loaded and no filler. The first sentence packs the output dimensions into a dense but meaningful list, and the second anchors usage with concrete examples. It earns a 4 rather than 5 because the long list of outputs makes it slightly heavier than necessary.

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?

There is no output schema, so the description carries the burden of explaining what comes back. It does name the main output components and the price-guide source, which is enough to understand the tool's purpose. Yet it omits practical details such as currency, data freshness, what happens when no year is provided, and what a result looks like, leaving moderate ambiguity.

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?

With 0% schema description coverage, the description must compensate. It partially does: it explains that year is optional ('de un año si se indica') and that 'version' refers to engine and gearbox groupings, and the Onix 2019 example illustrates model plus year. However, it does not explicitly define the brand parameter or give format/constraint guidance beyond what the schema already supplies.

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 that the tool provides asking-price statistics for a car model: median, range by year and version, a Mercado Libre price guide, model opportunities, and cheapest listings. The example queries ('¿cuánto vale un Onix 2019?', '¿a cuánto vendo mi auto?') make the purpose concrete. However, it does not explicitly distinguish this tool from siblings such as car_market_report or find_car_opportunities, so differentiation is implicit rather than stated.

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 provides clear trigger examples for valuation questions: how much a specific model is worth, and how much to sell a car for. This gives an agent usable guidance on when to invoke the tool. It stops short of a 5 because it never states when not to use it or which sibling tool to prefer instead.

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