Skip to main content
Glama

FalaZuki Finance BR

calculate_carne_leao

Calcula o carnê-leão do mês (DARF 0190): imposto sobre rendimento recebido de pessoa física ou do exterior sem retenção — aluguel direto do inquilino, honorários de autônomo, rendimentos do exterior — com livro caixa, dependentes, pensão paga e INSS. Aceita reference_date pra refazer um mês passado com a tabela da época. Parâmetros obrigatórios: monthly_income. Opcionais: book_expenses, dependents, alimony_paid, inss_paid, reference_date. Use exatamente estes nomes, em inglês.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inss_paidNoINSS de contribuinte individual pago no mês (a GPS)
dependentsNoDependentes na declaração
alimony_paidNoPensão alimentícia PAGA no mês por decisão judicial ou escritura pública
book_expensesNoDespesas do livro caixa no mês (aluguel do consultório, material, salários pagos) — só profissional autônomo
monthly_incomeYesRendimento tributável recebido no mês, em R$
reference_dateNoData de referência (aaaa-mm-dd) pra calcular com a tabela vigente NAQUELA data. Omitida, usa a de hoje.

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It usefully discloses that reference_date recomputes with the historical tax table ('refazer um mês passado com a tabela da época') and that deductions include book expenses, dependents, alimony and INSS. But it does not specify return format/units or mention how the progressive table applies to net taxable income, leaving some behavioral ambiguity.

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

Conciseness5/5

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

Three dense sentences in Portuguese: purpose+examples, historical behavior, and required/optional param names. No filler—each sentence earns its place, and the most distinctive information (DARF 0190, income types) is front-loaded.

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 description explains the calculation domain, the eligible income, the deduction items, the historical-table behavior, and the exact parameter names/requirements — a strong set for a 6-param calculator with no output schema. The only notable omission is an explicit statement of what the function returns (e.g., tax amount in R$), so it falls just short of a 5.

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 100% and each parameter has its own description, so the baseline is 3. The description adds a meta-instruction to use the exact English parameter names and marks monthly_income as required and the rest optional, which is helpful but largely restates the schema's required array and property names.

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 'Calcula o carnê-leão do mês (DARF 0190)' — a specific verb, tax form, and period — and enumerates the exact income types it applies to ('aluguel direto do inquilino, honorários de autônomo, rendimentos do exterior'). This level of specificity distinguishes it from generic DARF and RPA calculators among the siblings.

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?

It provides clear applicability context by listing the income sources and deductible items, so an agent can choose it for monthly carnê-leão obligations. However, it never names alternatives or states when not to use it (e.g., annual IR via simulate_income_tax), so it earns 4 rather than 5.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources