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Banco Central do Brasil (BCB) — SGS Time Series MCP Server

by io.github.SidneyBissoli

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 Banco Central do Brasil time series (SGS: interest rates, inflation, exchange rates, credit, fiscal and external sector — the curated catalog plus the open data portal index) catalog. Only ids returned by search are valid; an unknown id returns an error. The bcb_* tools 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…)
provenanceYesUm bloco por procedência que contribuiu com esta resposta (contrato v1.0; licenças nunca se fundem)
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 readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false. The description adds useful behavioral context beyond these: it performs a live GET against the public source when needed and errors on unknown ids. This is valuable but partly redundant with 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 well-structured: it leads with the return shape, then clarifies the tool's role and boundary, and ends with behavioral notes. The parenthetical catalog enumeration is a bit long but helps define the datasource; overall there is little waste.

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?

For a single-parameter fetch tool with a rich output schema and annotations covering safety and idempotency, the description is complete. It explains where ids come from, what the document contains at a high level, the source behavior, and the error condition, leaving no critical gap for correct invocation.

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 schema already covers the only parameter fully: `id` is described as an identifier returned by `search`. The description repeats this constraint and adds that unknown ids are invalid, but does not materially extend the meaning beyond what the schema provides. With 100% schema coverage, a 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 states a specific verb and resource: 'Returns the full document for an id obtained from `search`', with the exact response shape. It also distinguishes itself from siblings by identifying as the companion of `search` in the Deep Research contract and explicitly noting that `bcb_*` tools remain for data queries.

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?

Usage guidance is explicit: this tool is for ids returned by `search`, and unknown ids produce an error. It also names the alternative category (`bcb_*` tools) for data queries, so an agent knows when not to use this tool.

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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TDQS

A4.4/5.0
Disambiguation5/5

Every bcb_* tool targets a distinct operation—search, raw values, last values, metadata, variation, comparison, correlation, deflation, Focus expectations, exchange quotes, etc.—and the descriptions explicitly cross-reference 'use this instead' cases. The only near-overlap, search vs bcb_buscar_serie, is clearly separated by contract purpose: search returns documents for fetch, not data.

Naming Consistency3/5

The bcb_ prefix and snake_case are consistent, but the internal convention is mixed: some names are action-first (buscar_serie, deflacionar, comparar), some are resource-first (serie_valores, serie_metadados, cambio_cotacao), and some are noun-only (variacao, corrrelacao). The un-prefixed search/fetch also stand apart, though for a deliberate contract reason.

Tool Count3/5

With 17 tools, the surface is at the low end of the heavy range. Each tool has a defensible job and the breadth of BCB data (SGS series, exchange, Focus expectations, analytics, plus search/fetch) justifies many of them, but it is still more than the typical well-scoped 3–15 tool set.

Completeness5/5

The server covers the read-only domain thoroughly: discovery, browsing, search, metadata, raw values, recent values, variation, multi-series comparison, correlation, deflation, current indicators, exchange quotes, and Focus expectations with reference discovery. There are no dead ends—cross-references send agents to the right next tool—and no relevant CRUD operations are missing because this is a read-only data source.

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