Calcula o salário real descontando a inflação. Mostra se o reajuste acompanhou o IPCA ou se houve perda de poder de compra. Parâmetros obrigatórios: previous_salary, current_salary, inflation_period. Use exatamente estes nomes, em inglês.
| Name | Required | Description | Default |
|---|---|---|---|
| current_salary | Yes | Salário atual em R$ | |
| previous_salary | Yes | Salário anterior em R$ | |
| inflation_period | Yes | Inflação acumulada no período (%, ex: 5.5) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full behavioral burden. It explains the core calculation and comparison to IPCA, but it does not disclose the exact formula, output structure, or how edge cases like negative inflation are handled.
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?
The description is compact and well organized, leading with the core purpose, then the expected outcome, then the required parameter names. No sentence is wasted.
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?
The description covers the tool's purpose and required inputs, but since there is no output schema, it does not clarify the return format or the direction of the inflation adjustment. For a simple calculator this is acceptable, but additional detail about the result would make it more complete.
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?
The schema already describes all three parameters at 100% coverage, so a baseline of 3 applies. The description adds value by explicitly warning to use the exact English parameter names, which helps an agent avoid localization errors when invoking the tool.
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?
The description states a specific verb and resource: it calculates real salary by discounting inflation and indicates whether the adjustment kept pace with IPCA or lost purchasing power. This is clear, but it does not explicitly distinguish the tool from closely related siblings such as calculate_raise_vs_inflation or calculate_inflation_adjustment.
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?
The intended use case is implied through the outcome description: checking whether a salary adjustment kept up with IPCA. However, there is no explicit guidance about when to choose this tool over sibling alternatives or when not to use it.
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.
