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FalaZuki Finance BR

check_inss_deduction

Confere se um desconto de INSS de empregado CLT bate com o esperado pela tabela vigente NA COMPETÊNCIA informada (cobertura verificada desde ago/2006, incluindo a era de alíquota única pré-março/2020). Compara informado × esperado e devolve a diferença, a reconstrução faixa a faixa, a norma que fixou a tabela e a fonte oficial conferida. Diferenças de até R$ 0,10 são tratadas como arredondamento de folha, declaradas e nunca apagadas. Não conclui nada que não consiga reconstruir: fora da cobertura, recusa declarando o motivo. Parâmetros obrigatórios: base_salary, informed_deduction, reference_date. Use exatamente estes nomes, em inglês.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
base_salaryYesBase de cálculo do INSS na competência (no holerite aparece como 'Base INSS' ou 'Salário de contribuição'), em R$
reference_dateYesQualquer dia dentro da competência a conferir, aaaa-mm-dd
informed_deductionYesO desconto de INSS que consta no documento, em R$

TDQS

A4.4/5.0
Behavior5/5

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

No annotations are present, so the description carries the full burden — and it does so thoroughly. It discloses the comparison behavior, the returned elements (difference, band-by-band reconstruction, legal norm, official source), the R$0.10 rounding policy, and the refusal behavior outside coverage. This fully sets expectations for a read-only verification tool.

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?

The description is dense but every sentence adds operational detail: coverage, rounding policy, refusal behavior, and required parameters. There is minor redundancy between the opening 'bate com o esperado' and the later 'Compara informado × esperado', but overall it is efficient and front-loaded.

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

Completeness5/5

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

For a tool with no output schema and no annotations, the description covers inputs, expected outputs, edge cases (rounding and out-of-coverage), and historical coverage. No critical fact an agent needs to invoke it correctly or interpret its results 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%, with each parameter already described in Portuguese with constraints (e.g., base_salary exclusiveMinimum 0, reference_date pattern). The description only adds the directive to use the exact English parameter names, which is useful but does not deepen semantic understanding beyond what the schema provides.

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

Purpose5/5

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

The description opens with 'Confere se um desconto de INSS de empregado CLT bate com o esperado...' — a specific verb (check), resource (INSS deduction), and domain (CLT employee). The scope is further narrowed by reference competência and table era, making it clearly distinct from siblings like calculate_inss_autonomo or get_inss_table.

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

Usage Guidelines4/5

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

The description states the intended use: verifying a CLT INSS deduction against the applicable table for the informed competência, and it explicitly says the tool refuses outside coverage. It does not name alternatives or give explicit when-not-to-use guidance, so it provides clear context but not a full routing rule.

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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TDQS

B3.2/5.0
Disambiguation2/5

Many tools are clearly distinct, but the set contains several near-identical clusters: calculate_dividend_income_goal and calculate_dividend_yield both answer 'how much capital is needed to reach a dividend income target', and calculate_real_salary, calculate_raise_vs_inflation, and calculate_salary_time_value overlap heavily on salary/inflation comparisons. Generic tools like compare_investments and compare_with_cdb also blur the boundary with the many specific yield calculators.

Naming Consistency3/5

Most tools follow a clear verb_noun snake_case pattern with verbs like calculate_, get_, check_, compare_, and advise_. However, the object language is inconsistent (calculate_ganho_capital_imovel alongside calculate_car_affordability), and can_i_quit_job breaks the command-style pattern with a question.

Tool Count1/5

At 114 tools, the server is extremely over-scoped for an MCP surface; an agent cannot reasonably hold all these options in context. The inclusion of a search_calculator tool to route among the others is a strong signal that the tool set itself needs partitioning.

Completeness4/5

The surface is very comprehensive for Brazilian personal finance: employment, taxes, investments, debt, real estate, vehicles, small business, insurance, and market-data queries are all covered. A few minor gaps exist, such as no dedicated generic boleto-fine calculator or consolidated investment comparison engine, but no core workflow feels badly stranded.

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