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

The description discloses key behaviors: it is a paid consultation, the data is not confidential, and the client is the data controller per LGPD. It also notes the absence of platform credentials, which clarifies authentication. However, it omits details about error handling, rate limits, or the exact nature of the returned data, which could be expected in a complete description.

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 relatively concise, consisting of a short paragraph with relevant details. It communicates the essential points without unnecessary verbosity, though it repeats the idea of official source consultation twice. The structure is acceptable and 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?

The description provides some context such as the official source, payment model, and data classification, but it lacks critical information about expected output, error conditions, and prerequisites like having a prepaid balance. It also does not clarify the meaning of several parameters, leaving the user without a complete understanding of how to invoke the tool successfully.

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 input schema lists six parameters, but none have individual descriptions, and the tool description does not explain them. While placa (plate) and chassi (chassis) are self-explanatory, fields like a3 and a3_pin are unclear. login_cpf and login_senha are recognizable but their purpose is not elaborated. The lack of semantic explanation for these parameters significantly reduces usability.

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 performs a consultation of vehicle liens (gravames) from an official source. It explicitly identifies the resource and action, and the tool name reinforces this. No ambiguity exists about what the tool does.

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 contextual guidelines such as the payment model (prepaid credit), the official nature of the data, and LGPD compliance, implying when it might be used. However, it does not explicitly state when to use this tool over alternatives, though the sibling tools are unrelated and no direct alternatives exist.

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