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Glama

Comparar salário com faixas

comparar_salario

Determine which pay band a salary in Brazilian reais falls into by comparing it against a configurable JSON pay-band file, with an optional custom config path.

Instructions

Diz em qual faixa um salário se encaixa, segundo um arquivo JSON de faixas configurável (variável MCP_FAIXAS_PATH ou parâmetro caminhoConfig).

Input Schema

NameRequiredDescriptionDefault
salarioYesSalário em reais
caminhoConfigNoCaminho de um JSON de faixas; padrão: config/faixas-salariais.json

Output Schema

NameRequiredDescriptionDefault
faixaYes
indiceYes
arquivoYes
salarioYes
proximaFaixaYes
abaixoDaPrimeiraYes
faltaParaProximaYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It does disclose a genuinely useful behavioral detail: the bands come from an external JSON resolved via the MCP_FAIXAS_PATH environment variable or the caminhoConfig parameter. However it does not say what happens when the file is missing, malformed, or when the salary falls outside all bands, which are the main failure modes an agent would want to anticipate.

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?

A single sentence that front-loads the core purpose and then adds the configuration caveat, with no filler. It is efficient, though the parenthetical config detail makes the sentence slightly heavy for a two-parameter tool.

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 return shape need not be explained, and the input schema fully documents both parameters. For a simple lookup tool the description is nearly sufficient, with the only real gap being error behavior around the external config file.

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 both parameters are already documented in the schema, setting a baseline of 3. The description adds a little value by naming the environment variable MCP_FAIXAS_PATH that the configurable file may come from, which the schema does not mention, but it does not clarify precedence between the env var and the parameter.

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 and resource: it tells which salary band ('faixa') a salary falls into, and names the data source (a configurable JSON of bands). That is unambiguous and no sibling tool does anything similar, so no differentiation is needed. It stops just short of 5 because the phrase is dense and the notion of 'faixa' is left to the config file to define.

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 description implies the use case (classifying a salary against bands) but never states when to use it versus alternatives or any precondition such as the config file needing to exist. With unrelated siblings there is no routing ambiguity, so implied usage is adequate rather than deficient.

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