Salário médio de uma profissão no Brasil em CLT (mediana, p25, p75, média) com base em dados reais do CAGED do Ministério do Trabalho. Aceita nome ('programador'), slug ('analistas-de-sistemas') ou código CBO 4 dígitos ('2124').
| Name | Required | Description | Default |
|---|---|---|---|
| profession | Yes | Nome da profissão (ex: 'programador', 'enfermeiro', 'advogado'), slug ou CBO 4 dígitos |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations and no output schema, the description carries the full transparency burden. It discloses meaningful behavioral details: the data source (CAGED from the Ministry of Labor), the scope (Brazil, CLT), and the exact statistics returned (median, p25, p75, mean). This goes well beyond what the name alone suggests.
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 lead with the tool's purpose and output, then cover accepted input variants. There is no filler, no restatement of the tool name, and every clause earns its place.
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 one-parameter lookup with no output schema, the description is essentially complete. It defines what is returned, the geographic and employment scope, the authoritative data source, and all supported input forms. Nothing essential is missing for an agent to call the tool correctly.
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 input schema already covers the single parameter at 100%, so the baseline is 3. The description adds value by providing concrete examples for every accepted format, including a slug example ('analistas-de-sistemas') and a 4-digit CBO code ('2124'), which reduces ambiguity for the agent.
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 clearly identifies the verb and resource: it returns average salary statistics for a Brazilian profession under CLT. It also names the concrete outputs (median, p25, p75, mean) and the data source, which separates it from generic calculation tools and price lookups 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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by saying it handles profession salary lookups in Brazil, and it explains the accepted input formats. However, it gives no explicit guidance on when to prefer this tool over related salary siblings such as calculate_net_salary, compare_clt_vs_pj, or list_top_professions.
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.
