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Documento para Deep Research

fetch
Read-onlyIdempotent

Returns the full document for an id obtained from search, as { id, title, text, url, metadata }: text is the readable content (Markdown) and url the canonical public page to cite.

Companion of search in the OpenAI Deep Research contract, over the IBGE (Brazilian official statistics: SIDRA tables, municipalities, known indicators) catalog. Only ids returned by search are valid; an unknown id returns an error. The ibge_* tools (ibge_sidra, ibge_cidades, ibge_indicadores, ibge_comparar…) remain the tools for data queries.

Behavior: read-only and idempotent — a live GET against the public source when the document needs it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesIdentificador de um documento devolvido por `search`

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesIdentificador único do documento no servidor; é o que `fetch` recebe
urlYesURL pública canônica do documento — a citação do ChatGPT depende dela
textYesConteúdo integral do documento, legível (Markdown)
titleYesTítulo legível do documento
metadataNoPares chave/valor adicionais sobre o documento (tipo, fonte, período…)
provenanceYesBloco de proveniência (contrato v1.0): fonte, URL, período, extração e licença
attributionYesURLs canônicas das fontes desta resposta (lista de atribuição)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, and open-world hints. The description adds a concrete behavioral detail: 'a live GET against the public source when the document needs it' and notes error handling for unknown ids. This supplements, not contradicts, the annotations.

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 structured into three focused paragraphs: purpose, context/usage, and behavior. While slightly verbose with the 'OpenAI Deep Research contract' phrase, each sentence contributes useful information and the main purpose 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?

With an output schema available and a single parameter, the description covers all necessary context: the source catalog (IBGE), the prerequisite of `search`, the error case, and the tool's role relative to siblings. Nothing an agent needs to invoke it correctly is missing.

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?

The sole parameter `id` is fully described in the schema (100% coverage) as a document identifier from `search`. The description reinforces this dependency and error condition, but adds little new semantic value beyond the schema's explanation. Baseline 3 is appropriate.

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 action: 'Returns the full document for an id obtained from `search`' — a specific verb, resource, and dependency. It distinguishes itself from the ibge_* siblings by explicitly noting they are 'the tools for data queries', preventing confusion.

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?

The description provides explicit usage guidance: ids must come from `search`, unknown ids produce an error, and the ibge_* tools are the alternative for data queries. This leaves no ambiguity about when to invoke this tool versus others.

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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