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

The description goes well beyond the annotations (readOnlyHint=true, idempotentHint=true, destructiveHint=false) by disclosing the payment model (pay per query with prepaid credit), the absence of platform credential needs, the data sensitivity classification (not confidential but LGPD-governed), and the data controller responsibility. This significantly enriches the agent's understanding of side effects and legal constraints.

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?

Four sentences front-load the core purpose and then cover payment, data scope, and legal responsibility efficiently. Each sentence adds new information, though the legal phrasing around LGPD and data controller feels slightly verbose and could be condensed.

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?

The description excels at clarifying the business and legal context (pricing, data nature, compliance), but fails to address the most operationally critical aspect: input parameters. With three parameters, zero documentation coverage, and no output schema, the agent cannot reliably construct a valid request. The lack of any parameter semantics makes this incomplete for a 3-parameter tool.

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%, and the description fails to compensate. None of the three parameters (cpf, cnpj, renavam) are explained or even mentioned, leaving the agent without any guidance on what these mean or what format they should take. The required 'renavam' parameter remains entirely opaque, which is a critical gap for correct invocation.

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 queries 'Multas (Guia de Recolhimento de Multas)' from DETRAN RJ official sources, using the specific verb 'consulta'. It distinguishes itself from utility siblings like 'authenticate' or 'report_bug' by focusing on a specific data consultation (fines) within a specific domain (DETRAN RJ).

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 implies usage context by noting it's a paid per-query service with prepaid credit and LGPD compliance responsibilities, but it does not explicitly state when to use this tool instead of alternatives. For a niche data query tool among generic utilities, the paid/source context is helpful, but 'when not to use' and explicit alternatives are missing.

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