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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds genuinely complementary behavioral context: the cost-per-query model with prepaid credit, the fact that it surfaces only citizen-available (non-confidential) data, and the client's LGPD data-controller responsibilities. One could argue it leans toward legal boilerplate rather than operational behavior, but the cost and data-origin disclosures add real value beyond the annotations.

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 sentences covering purpose, commercial model, and compliance fit in a compact block with no wasted words. However, the single-paragraph format buries operational info amid commercial/LGPD boilerplate, and there's no front-loading of the most decision-relevant facts. Acceptable but unpolished structure.

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 simple tool with 2 flat string parameters, no enums, no output schema, and strong annotations, the coverage is adequate but not thorough. The description covers data source, cost, and legal ownership but leaves gaps on input format expectations and what the response contains. Given the simple tool complexity, this is a reasonable if not generous score.

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 and two parameters (renavam required, anos_anteriores optional), the description was expected to compensate but never mentions either parameter. The names are somewhat self-explanatory by Brazilian domain convention (renavam = vehicle registration number; anos_anteriores = previous years), but the description adds zero explanation of formats, expected values, or how to specify multiple previous years. The naming convention and common domain knowledge do most of the work, which is why this isn't a 1.

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 identifies the tool as a query ('consulta') of IPVA data from SEFAZ DF, an official Brazilian source, matching the tool name's verb+resource. The phrase 'SEFAZ DF: IPVA, consulta em fonte oficial' provides specific verb+resource clarity. It loses a point for not explicitly stating what kind of result to expect and for requiring the reader to infer the core function from the name plus description.

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 useful usage context: it's paid per query with prepaid credit, requires no platform credentials, and the client is responsible for legitimate purpose (LGPD). However, it provides no explicit when-to-use vs alternatives, no exclusions, and no distinction from other data-querying tools. The LGPD note implies appropriate usage contexts but stops short of explicit guidance.

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