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ab-ibge-mcp-server

by ab-orbit

Estimativas Populacionais Anuais

ibge_estimativas_populacionais
Read-onlyIdempotent

Retrieve Brazilian population estimates for any year between 2001 and 2024, including post-census data. Use it to obtain figures for states, municipalities, or the entire country.

Instructions

Consulta estimativas de população para anos entre censos (2001-2024+). As estimativas são usadas pelo TCU para repasse de verbas e são atualizadas anualmente.

Args:

  • ano (obrigatório): Ano da estimativa (ex: 2024, 2023, 2022...)

  • localidade (opcional): "BR" (Brasil), "estados" (todos estados), ou código IBGE específico

  • municipio_id (opcional): Código IBGE de 7 dígitos para consulta de município específico

Use para dados mais recentes que o Censo 2022 ou para anos intermediários. Exemplo: População estimada de 2024, população de município não finalizado no censo.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
anoYesAno da estimativa
localidadeNoBR=Brasil, estados=todos estados
municipio_idNoCódigo IBGE do município (7 dígitos)
Behavior3/5

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

Annotations already declare readOnly, idempotent, and non-destructive behaviors, so the description has a lower burden. It adds context about TCU usage and annual updates, but doesn't disclose response format, default behavior when optional params are omitted, or error handling. Adequate but not rich.

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

Conciseness4/5

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

The description is well-structured with a clear first sentence and a bulleted parameter list. It is concise and front-loaded with purpose, though the final example sentence adds a bit of redundancy without much new information.

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

Completeness2/5

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

The description covers purpose, scope, parameters, and usage guidance, but the schema contradiction regarding localidade creates a critical completeness gap. An agent cannot reliably invoke the tool based on this description, so it falls short of being complete.

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

Parameters1/5

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

The description adds examples and explanation, but it explicitly states localidade can be an IBGE code while the input schema restricts it to an enum of 'BR' and 'estados'. This direct contradiction misleads the agent and will likely cause invalid calls, making the description worse than merely relying on the schema.

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 clearly states the tool queries annual population estimates for inter-census years (2001-2024+), distinguishing it from sibling tools like the 2022 census. The TCU context and annual update cadence reinforce a specific, well-scoped purpose.

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

Usage Guidelines4/5

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

The description explicitly says 'Use para dados mais recentes que o Censo 2022 ou para anos intermediários', providing clear context on when to choose this tool. It doesn't name alternative tools or exclusions, but the scope is defined well enough for an agent to make an informed 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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