Lista os meses de referência da Tabela FIPE disponíveis no índice (~2001→hoje). Retorna [{ codigo, mes, is_current }]. Use o codigo como referencia em fipe_preco/fipe_historico para consultar um mês específico.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: the date range (~2001→hoje), the return structure with fields (codigo, mes, is_current), and the meaning of is_current. No contradictions.
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 sentences, front-loaded with purpose, then return format and usage. Every sentence adds value without waste.
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?
Given no parameters and no output schema, the description is complete. It explains what the tool does, what it returns, and how to use it with sibling tools. No missing information.
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 baseline is 4. The description adds meaning beyond the schema by explaining the return format and how the output is used, which is sufficient.
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 states the tool lists reference months of the FIPE table from ~2001 to today, and specifies the return format. It distinguishes itself from siblings by explaining how its output (codigo) is used as a parameter in other tools like fipe_preco and fipe_historico.
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 explains when to use the tool (to get reference months for price queries) and how to use its output (use codigo as referencia in other tools). While it does not explicitly list when not to use it, the context is clear and sufficient for an agent to decide.
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.
The seven fipe_* tools each target a distinct step in the FIPE data lookup (brands, models, years, price, history, search, reference dates), and their names make their purposes clear. However, connect and toolkit_info both report connection status, and marketplace bundles many sub-operations into one tool, creating minor ambiguity.
FIPE tools follow a consistent fipe_<noun> pattern, but the platform tools (authenticate, connect, marketplace, report_bug, show_version, toolkit_info) use generic verbs/nouns without a shared convention, mixing styles within the same server.
13 tools is within the ideal range for a domain-specific data server; the 7 FIPE tools are well-scoped and the 6 platform utilities are justified for authentication, marketplace access, and system status.
The FIPE data surface is complete: marcas → modelos → anos → preco covers the full drill-down, with buscar, historico, and tabelas_referencia adding search, time series, and date selection. No obvious missing operations for the domain.