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Babá Certa

Mostrar perfis e custo

mostrar_busca_e_custo
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Consulta perfis públicos consentidos e calcula custo de contratação em um cartão interativo com detalhes ajustáveis. Exige cidade e UF informadas pela pessoa na conversa. Refaz a consulta e o cálculo para exibir dados atuais. Sem salário bruto informado, usa referência estadual vigente identificada como exemplo. Não calcula distância nem classifica a qualidade das profissionais.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ufYesSigla da unidade federativa, por exemplo SP.
cidadeYesCidade brasileira informada pela pessoa, sem endereço ou bairro.
diasUteisMesNoDias úteis no mês; se não informado, 22.
salarioBrutoNoSalário bruto mensal em reais.
valeTransporteDiarioNoCusto diário de vale-transporte em reais; se não informado, zero.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
costYes
kindYes
searchYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint true, destructiveHint false, openWorldHint true), and the description is consistent with them. It adds real behavioral context beyond the annotations: it re-runs query and calculation for fresh data, and falls back to the state salary reference when no gross salary is supplied.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose is front-loaded in the first sentence, and the following sentences each carry a distinct fact (prerequisite, refresh behavior, fallback, exclusions). Five short sentences with no filler, though the negative-scope sentence could arguably be tightened.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has an output schema, so return values need no explanation, and annotations cover safety. The description supplies prerequisites, fallback behavior, and scope boundaries, leaving little an agent needs that is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all five parameters including defaults. The description reinforces the city/UF requirement and explains the salary fallback, which adds a little beyond the schema, but it still meets the baseline-3 case where the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States two specific verb+resource pairs ('consulta perfis públicos consentidos' and 'calcula custo de contratação') and names the output form ('cartão interativo com detalhes ajustáveis'). It implicitly distinguishes itself from the single-purpose siblings buscar_babas_publicas and calcular_custo_baba by doing both at once, but never says so explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

'Exige cidade e UF informadas pela pessoa na conversa' gives a prerequisite, and 'Não calcula distância nem classifica a qualidade' gives scope exclusions. However, it never states when to pick this combined tool over the individual search/cost siblings, so routing remains inferred.

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

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