senado_pessoal_tabelas

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds significant behavioral details: return format with fields like tabela, count, total, aviso, registros; explanation of empty results (count 0 and empty list); filtering behavior; and limit constraints (default 100, max 2000). No contradiction with annotations.

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 a dense single paragraph that efficiently packs all necessary information. While it is concise and avoids fluff, it could benefit from structured sections for readability. Nevertheless, it earns its place with every sentence.

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 (3 parameters, one required with enum, output schema described), the description covers all aspects: tool purpose, all table values, return structure, edge cases (empty results), filtering, limit constraints, and cross-reference to sibling tool. The output schema existence reduces the need to detail return values, but the description still provides a clear summary.

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 coverage is 100%, but the description adds meaning beyond the schema by explaining the semantic categories of tabela (quantitative vs. lists), how they affect the response structure, and the purpose of filtro (textual match against any field) and limite (max records). This additional context helps the agent understand parameter usage.

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 Senate personnel tables, listing all eight table values and distinguishing between quantitative aggregates and nominal lists. It also explicitly differentiates from a sibling tool (senado_servidores) by directing to it for a specific use case.

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 clear context on what each table value represents and when to use the tool. It includes an explicit exclusion: for nominal registration of servers, use senado_servidores. However, it does not explicitly state 'use this tool when you need X' but the purpose is well understood from the content.

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