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

Annotations already signal read-only, idempotent, non-destructive behavior. The description adds valuable context beyond those hints: no platform credentials required, prepaid credit per query, data is public/non-confidential, and LGPD controller responsibilities. This meaningfully expands the behavioral picture, though it omits operational details like response format or failure modes.

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 reasonably concise and front-loaded with the core purpose. It includes necessary caveats (payment, credentials, LGPD) that add value rather than fluff. The legal sentence could be trimmed for an AI-focused audience, but it is still appropriately sized for the tool's complexity.

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 one-parameter, read-only tool, the description covers purpose, data source, auth, payment, and data classification. However, it lacks parameter guidance and does not describe what the returned registration information includes. Since there is no output schema, some explanation of the expected result would improve completeness.

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 has one required string parameter, `inscricao`, with 0% schema description coverage. The description does not mention `inscricao` at all, nor clarify what value should be supplied (e.g., registration number format or examples). Given the low schema coverage, the description should compensate, and it does not.

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 this tool consults registration data from the official Regional Dentistry Council of Amazonas (CRO-AM). The verb 'consultar' and resource 'cadastro' are specific, and the domain distinguishes it from the generic sibling tools (authenticate, marketplace, etc.). It could be slightly more explicit, but the purpose is unambiguous.

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 context: it queries official Brazilian public sources, the data is not confidential, and it is the same data available to citizens. It does not explicitly name when-not-to-use alternatives, but the sibling tools are unrelated, and the official-data framing offers adequate guidance. A brief exclusion note (e.g., 'not for private/sensitive data') would push it higher.

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