Calcula o consumo de energia de uma lista de aparelhos em kWh e o custo mensal e anual, a partir da potência em watts e das horas de uso por dia. Parâmetros obrigatórios: appliances, price_per_kwh. Use exatamente estes nomes, em inglês.
| Name | Required | Description | Default |
|---|---|---|---|
| appliances | Yes | Aparelhos a somar | |
| price_per_kwh | Yes | Preço do kWh em R$, com impostos e bandeira. Varia por distribuidora; olhe na conta de luz. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears the full burden of behavioral disclosure. It states the calculation inputs and outputs, but does not disclose any rounding behavior, units handling beyond kWh, order of operations, edge cases (e.g., zero watts), or whether the calculation assumes a standard month length. There is no mention of what happens with invalid inputs or how results are returned.
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 two concise sentences. The first sentence front-loads the purpose and scope, and the second provides essential invocation details. Every word earns its place with no redundancy.
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 calculation tool with two top-level parameters and no output schema, the description covers the inputs and outputs adequately. It specifies the required parameters and their units, which is sufficient for an agent to invoke it correctly. Minor gaps like explicit formula assumptions (e.g., monthly = 30 days) are not critical given the low complexity.
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%: the input schema fully describes both parameters, including required fields within the appliances array and valid ranges for watts and hours_per_day. The description merely repeats the parameter names without adding semantic meaning beyond what the schema provides, so the baseline of 3 applies.
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 precise verb ('calcula'), a specific resource ('consumo de energia de uma lista de aparelhos'), and the calculated outputs (kWh, monthly and annual cost). It clearly differentiates from the many sibling calculate_* tools by naming the exact input domain (watts and hours per day).
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 explicitly names the two required parameters (appliances, price_per_kwh) and instructs the agent to use exactly these English names, which is helpful for correct invocation. However, it does not provide when-to-use vs. alternative tools or exclusion conditions, though the specificity of the purpose makes alternatives unlikely.
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.
