Retorna a tabela de contribuição INSS com faixas e alíquotas progressivas. Aceita year (2025+) pra tabela histórica; sem year, a vigente hoje.
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | Ano da tabela desejada (ex: 2025). Omitido, a vigente hoje. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral burden. It openly discloses what is returned (tabela com faixas e alíquotas progressivas) and how the optional year changes behavior (historical vs current). This is adequate for a simple read-only lookup, though it doesn't describe the exact output structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with no filler. The main purpose is stated first, and the optional-parameter guidance is immediately relevant and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-optional-parameter lookup tool with no output schema, this is complete. The agent knows what the tool returns, how to get the current table, and how to get a historical table, which is sufficient for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds meaningful semantics beyond the schema: it clarifies that year is for historical tables from 2025+ and that omission returns the current table. This adds value, even though it leaves ambiguous behavior for years between 2000 and 2024 allowed by the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource: returns the INSS contribution table with progressive rate bands. It also distinguishes historical vs current table semantics, which separates it from siblings like calculate_inss_autonomo or check_inss_deduction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Clearly explains when to omit year (use current table) and when to provide year (historical table from 2025+). It does not explicitly name alternatives or exclusion conditions, but the context is strong enough for the agent to select this tool correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.
