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

The description adds significant context beyond the annotations: since the tool is read-only (readOnlyHint: true) and idempotent (idempotentHint: true), the description complements with usage nuances. It clarifies that data is public, no platform credentials are required, usage is billed per query with prepaid credits, and includes LGPD privacy disclaimers. This is a solid addition, though it still leaves some aspects, like rate limits or failure behavior, undisclosed.

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 generally concise and structured, with a single clear message about the tool's purpose and an important note about data usage. The mention of LGPD and commercial terms is useful context but is slightly lengthy. Overall, every part earns its place, though some information could be tightened for consistency.

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 the tool's complexity (6 parameters, 4 required), the description covers many aspects: it states that the data is public, explains the payment model, and quotes the LGPD privacy law, which are important for the agent to comply with. However, it does not explain expected return values or behavior beyond 'consulta', and the lack of output schema means that return format is not specified. The description is adequate for the agent to use the tool correctly, though it could benefit from clarifying response behavior.

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 provides parameter names (query, orgao, situacao, modalidade, etc.) with no descriptions or enums, and the tool description has 0% schema coverage. However, some parameters (e.g., orgao, modalidade) are self-explanatory in the procurement context, and the description's mention of 'consulta' implies search behavior. Still, the description provides no explicit guidance on how parameters should be filled, leaving the agent to infer semantics from the schema names alone. Since schema coverage is at 0%, the description does not compensate for the lack of parameter documentation.

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 tool for public procurement (licitações) from the official source of Perolândia/GO, using the verb 'consultar' (consult), which fits the resource. Although it does not explicitly differentiate itself from sibling tools, it does state that it provides information from official Brazilian sources, which is helpful context. The description could be more direct, but it successfully communicates the tool's core function.

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 states that the tool should be used for consulting public bidding information from Perolândia and that it is powered by the platform, requires prepaid credits, and accesses public (non-confidential) data. However, it does not explicitly clarify when to use this tool as opposed to other sibling tools or when not to use it. It explains the payment and data privacy context but lacks explicit exclusions or alternative scenarios.

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