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Calcular salário líquido

calcular_salario_liquido
Read-only

Estima o salário líquido mensal de uma babá a partir do salário bruto, dependentes e vale-transporte informados. Retorna INSS, IRRF, transporte, fontes e premissas. Não presume dados da conta nem interpreta vínculos individuais.

Input Schema

NameRequiredDescriptionDefault
dependentesNo
diasUteisMesNoDias úteis no mês; se não informado, 22.
salarioBrutoYesSalário bruto mensal em reais.
valeTransporteDiarioNoCusto diário de vale-transporte em reais; se não informado, zero.

Output Schema

NameRequiredDescriptionDefault
kindYes
caveatYes
statusYes
messageYes
sourcesYes
basisYearYes
breakdownYes
dependentesYes
diasUteisMesYes
salarioBrutoYes
valeTransporteDiarioYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • removedInput schema / additionalProperties
      Removed value: -false
  2. First observed

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds useful behavioral context by naming the returned components (INSS, IRRF, transporte, fontes, premissas) and the non-presumption caveat, but says nothing about rounding, tax-table vintage, or whether results are estimates vs. authoritative figures — significant for a payroll calculation.

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?

Three sentences with no filler: purpose, return contents, and scope caveat, in that order. Efficient and front-loaded, though the return list slightly overlaps the output schema.

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?

An output schema exists, so the description need not detail return values, and annotations cover safety. What remains missing is any note on diasUteisMes defaulting behavior and whether the estimate reflects current tax tables — minor but relevant for an agent deciding trust in the numbers.

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 coverage is 75%, so most parameters are documented structurally; the description merely names salário bruto, dependentes and vale-transporte, restating the schema. It omits diasUteisMes entirely, which is the one parameter that materially changes the transporte computation, so it adds no meaning beyond the schema.

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 a specific verb (estima) and resource (salário líquido mensal de uma babá) plus the inputs that drive it. It does not explicitly differentiate itself from the sibling calcular_custo_baba or mostrar_salario_liquido, which is the only thing keeping it from a 5.

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?

The closing sentence 'Não presume dados da conta nem interpreta vínculos individuais' establishes a scope boundary, hinting at when the tool is not appropriate. However, it never names an alternative tool or states the positive trigger condition versus calcular_custo_baba, leaving the choice to inference.

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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