brazil-macro-ai

brazil_macro_actual

Returns a Brazilian macro headline actual (IPCA, IPCA-15, GDP, PIM-PF industrial production, PMC retail sales, PMS services volume, or PNAD Contínua unemployment) with full release-vintage provenance — built on a release/vintage layer that NEVER overwrites an original published number when a later revision arrives (method_id macro_actual_v1).

Use when: you need the current headline reading for a Brazilian macro
indicator, or (via `vintage`) what was known at a specific historical
instant vs. what is currently the revised/correct value.
Do not use when: you want the surprise vs. consensus (use
brazil_macro_surprise, IPCA only) or the release schedule (use
brazil_macro_calendar).

Args:
    indicator: one of IPCA, IPCA15, GDP, PIM_PF, PMC, PMS, PNAD_UNEMPLOYMENT.
    reference_period: e.g. "2026-07" (monthly) or "2026-Q2" (GDP).
    as_of: YYYY-MM-DD, REQUIRED when vintage="as_known".
    vintage: "latest" (currently-correct value, default), "first"
        (original release, revision_number=0), or "as_known" (what was
        known publicly at `as_of`).

Input Schema

TableJSON Schema

Name	Required	Default
`as_of`	No
`vintage`	No	latest
`agent_id`	No
`indicator`	Yes
`session_id`	No
`payment_token`	No
`discovery_source`	No
`reference_period`	Yes
`synthetic_identity`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 14, 2026

TDQS

A4.7/5.0

Behavior4/5

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

With no annotations, the description carries the behavioral burden and does it well: it discloses the never-overwrite revision policy, defines latest/first/as_known vintage modes, and explains that as_known means what was publicly known at a date. It stops short of discussing access/payment or read-safety explicitly, though 'Returns' and the non-overwrite policy strongly imply a read-only operation.

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?

The prose is dense but each sentence earns its place: purpose, provenance guarantee, usage rules, and argument semantics are separated into clear blocks. The most important scope and exclusion information is front-loaded before the arg list.

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 tool with nine parameters and non-trivial vintage semantics, the description explains the core selection and versioning behavior completely. An output schema is present, so detailed return-value documentation is not the description's responsibility; the remaining gaps are only generic bookkeeping parameters.

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

Parameters4/5

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

Schema coverage is 0%, so the description must compensate. It lists all valid indicator values, gives concrete reference_period formats for monthly and quarterly, and clarifies as_of as required only for vintage='as_known'. The remaining optional/metadata parameters (agent_id, session_id, payment_token, etc.) are left for the agent to infer from names, but they are not central to the tool's behavior.

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?

Description opens with a specific verb-resource pair ('Returns a Brazilian macro headline actual') and enumerates the exact indicators covered. It also differentiates from siblings by naming the surprise and calendar tools it is not, so an agent can route correctly.

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?

Provides explicit 'Use when' and 'Do not use when' guidance, including which sibling to use for consensus surprises and release schedules. This gives an agent clear decision criteria beyond the tool name alone.

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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