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ibge_municipios

Read-onlyIdempotent

List Brazilian municipalities by state or search by name. Returns IBGE code and details for over 5,570 municipalities.

Instructions

Lists Brazilian municipalities from IBGE.

Features:

  • List municipalities by state (using state abbreviation)

  • List all municipalities in Brazil (5,570 municipalities)

  • Search by municipality name

  • Returns 7-digit IBGE code

Examples:

  • São Paulo municipalities: uf="SP"

  • Search by name: busca="Campinas"

  • MG municipalities containing "Belo": uf="MG", busca="Belo"

Use a different tool when:

  • Resolve/decode a code at any level (region, state, district), not just municipalities → ibge_geocodigo

  • Full details/hierarchy of one locality by code → ibge_localidade

  • Neighboring municipalities → ibge_vizinhos

Behavior: read-only and idempotent — a live GET against the public IBGE Localidades API. Returns a Markdown table.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ufNoEstado por sigla (SP), nome (São Paulo) ou código IBGE (35). Se não informado, retorna todos os municípios do Brasil.
buscaNoTermo para buscar no nome do município
limiteNoNúmero máximo de resultados (padrão: 100, máximo: 5570)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
municipiosYesLista de municípios retornados (após filtro e limite)
totalYesTotal de municípios encontrados antes do limite
ufNoUF informada no filtro (como recebida na entrada)
buscaNoTermo de busca aplicado ao nome do município
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint, destructiveHint, idempotentHint, and openWorldHint. The description adds that it is a live GET against the public API and returns a Markdown table, enriching the behavioral context without contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with clear bullet points, examples, and usage guidance. Every sentence contributes value, and key information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema (mentioned in context), the description adequately covers all aspects: purpose, parameters, behavior, and output format (Markdown table), making the tool fully understandable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with detailed descriptions in Portuguese. The description provides examples (uf='SP', busca='Campinas') but does not add significant new meaning beyond what the schema already offers.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description starts with 'Lists Brazilian municipalities from IBGE' and elaborates with specific features and examples, clearly differentiating from siblings like ibge_geocodigo, ibge_localidade, and ibge_vizinhos.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly details when to use this tool vs. alternatives: resolving codes -> ibge_geocodigo, full details -> ibge_localidade, neighbors -> ibge_vizinhos, providing clear context for selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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