Frequência Histórica de Nomes

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

Annotations already declare the operation as read-only, idempotent, and non-destructive. The description adds valuable behavioral context by specifying that the tool returns Censo data with frequency organized by decade, covers data since 1930, and clarifies the default for localidade. It does not mention potential edge cases or return format details, but for this tool the added context is sufficient.

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 well-structured with distinct sections for arguments, return value, and an example. It is concise, with no redundant or filler content. The action is front-loaded in the first sentence, and each subsequent detail earns its place by clarifying parameter meanings and usage.

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?

Although there is no output schema, the description adequately describes the return value as Censo data with frequency by period (decade) and provides an example. It could be more explicit about the exact structure of the response (e.g., list of decades with counts), but for this straightforward tool, the provided information is sufficient for an agent to understand what the tool does and what it returns.

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 already provides descriptions for all three parameters (100% coverage). The description adds value beyond the schema by including concrete examples for nome, stating the default for localidade ('BR'), and clarifying that sexo can be null to represent both genders, which is not apparent from the enum alone. This extra semantic detail helps the agent select and format parameter values correctly.

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 a specific action: returning the frequency of a given name in Brazil by decade since 1930. It also provides a concrete example question that the tool answers. However, it does not explicitly distinguish this tool from its sibling ibge_nomes_ranking, which likely serves a related but distinct purpose.

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 an example query ('Quantas pessoas se chamam João no Brasil?') that implies the intended use case, but it does not explicitly state when to use this tool over alternatives like ibge_nomes_ranking. There are no clear when-to-use or when-not-to-use guidelines, leaving the agent to infer appropriateness from context.

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