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

Beyond the annotations, it discloses the linkage policy (only norms ANVISA itself publishes; an older revised norm appears as prior, not result), the licensing prerequisite, that base rates are an 'estimativa', and that results are deterministic from official datasets with no model in the loop. These are exactly the caveats an agent needs to interpret the answer correctly.

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?

Front-loads the resource and enumerates returned fields before the usage example and constraints. The single dense sentence is efficient, though the parenthetical about prior norms makes it slightly hard to scan; no sentence is pure filler.

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 one-parameter, read-only lookup with no output schema, the description enumerates the returned fields (dates, window, norm, days elapsed, base rate) and the edge case of prior vs. resulting norms. An agent has enough to call it correctly and interpret the response.

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 only one parameter at 100% schema description coverage, the schema already documents the accepted consultation identifier formats. The description reinforces the semantics through an example ('did CP 1.234/2023 become a rule') but adds no resolution rules, normalization behavior, or error handling beyond the schema, so baseline 3 applies.

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?

States a precise verb and resource: for one ANVISA consulta pública it returns opening date, contribution window, whether a norm resulted, the norm and date, days elapsed, and base rates. The scope explicitly excludes mis-attributing a revised older norm as the consultation's result, which lets an agent distinguish it from siblings like regulatory_events or base_rates without opening any schema.

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?

Gives a concrete invocation scenario ('did CP 1.234/2023 become a rule, and how long did it take') and a prerequisite ('Needs the pharmaceutical sector licensed'). It does not name when to use base_rates or regulatory_events instead, so alternatives are left implicit rather than explicitly routed.

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