Retorna as projeções do Boletim Focus do Banco Central para Selic, IPCA, IGP-M, dólar, PIB e desemprego, por ano de referência, com mediana e dispersão. São EXPECTATIVAS de mercado, não dados observados.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It clearly communicates that the returned values are market projections (not observed data), and specifies the aggregation details (mediana e dispersão) and reference period structure (por ano de referência). It does not mention format, source freshness, or update cadence, but for a parameterless read tool this is a minor gap.
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 tight sentences with no filler. The first sentence front-loads the resource, indicators, and aggregation; the second immediately clarifies the crucial expectation-vs-observed distinction. Every clause adds value.
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 simple parameterless tool with no output schema or annotations, the description is nearly complete: it states the data source, the indicators, the aggregation, and the temporal dimension. It could slightly improve by noting the source's update cadence or that the result is a list, but nothing essential is missing 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?
The tool has zero parameters, so the baseline is 4. The description does not need to explain parameter semantics because there are none, and it focuses on what data is returned instead.
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 names a specific verb ('Retorna') and resource ('projeções do Boletim Focus do Banco Central'), and enumerates the covered indicators (Selic, IPCA, IGP-M, dólar, PIB, desemprego). It also distinguishes itself from observed-data tools by emphasizing these are market expectations, so an agent can tell it apart from sibling tools like get_rates_history or get_current_rates.
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 description clearly implies when to use it: when the agent needs Focus market expectations, not observed economic data. It states the exclusion ('não dados observados'), which helps avoid misuse, though it does not explicitly name alternatives or provide a when-when-not matrix.
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.
