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

Annotations already mark it as read-only, idempotent, and non-destructive, and the description does not contradict these. It additionally discloses important behavioral details like the segment-based ranking and the exact denominator field, providing full transparency.

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 dense but every sentence conveys a distinct, necessary fact. There is no redundancy or filler; the structure moves from general behavior to specific argument combinations, making it easy to parse.

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?

Given there is no output schema, the description compensates well by explaining the different result shapes for each argument combination and highlighting the denominator field. It is complete for a tool of this complexity.

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 schema descriptions cover all five parameters, and the tool description adds meaningful semantics: 'marca' alone returns totals, 'modelo' is only valid with 'marca', and pagination defaults are specified. This goes well beyond the basic 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?

The description clearly states the tool's function: querying 0km vehicle registrations for a reference month. It distinguishes from sibling tools by focusing on sales/registrations data rather than specs, pricing, or consumption, and clarifies that the result depends on which arguments are passed.

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 explains the three invocation modes (no args, marca only, marca+modelo) and what each returns. Also clarifies the denominator scope and the model-based catalog link, leaving no ambiguity about when and how to use the tool.

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