brazil-macro-ai

brazil_inflation_breakeven

Market-implied Brazilian inflation breakeven — Fisher's EXACT identity ((1+nominal)/(1+real) - 1), never the linear approximation — computed only where the nominal and real curves both have support at the same tenor (method_id breakeven_fisher_v1). Returns nominal_zero_rate_pct and real_zero_rate_pct alongside the breakeven so the result is auditable.

IMPORTANT: this is NOT "expected inflation" — it embeds risk premium,
liquidity and convexity effects. The response field is deliberately
named `market_implied_inflation_breakeven`.

Use when: assessing what the market is pricing for future inflation,
as distinct from Focus survey expectations (use
brazil_focus_revisions with indicator="IPCA" for that).
Do not use when: you need either curve alone (use brazil_yield_curve /
brazil_real_curve).

Args:
    as_of: optional YYYY-MM-DD. Defaults to today.
    tenors: comma-separated subset of 1Y,2Y,3Y,5Y,7Y,10Y. Default: all.
    quote_side: "buy", "sell", or "mid" (default).
    payment_token: required in staging ($0.03/call per pricing.yaml).

Input Schema

TableJSON Schema

Name	Required	Default
`as_of`	No
`tenors`	No
`agent_id`	No
`quote_side`	No	mid
`session_id`	No
`payment_token`	No
`discovery_source`	No
`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

A4.9/5.0

Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and handles it well. It discloses the exact identity used, the fact that this is not 'expected inflation', the risk premium/liquidity/convexity caveat, the support condition on both curves, and the auditable output fields.

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 description is organized with a compact definition, key caveat, usage instructions, and an Args section, with no redundant filler. Every section adds decision-relevant information and the most important scoping details are 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?

The tool has no annotations, but the description covers the computation, scope, alternatives, parameter semantics, output fields, and pricing in one place. Since an output schema exists, lack of detailed return-value documentation is acceptable, and nothing essential for correct invocation is missing.

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 descriptions are entirely absent, so the description compensates by documenting as_of, tenors, quote_side, and payment_token with formats, defaults, and restrictions. However, four schema parameters (agent_id, session_id, discovery_source, synthetic_identity) are not addressed in the description, though they appear to be common context fields.

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 identifies a specific resource (Brazilian inflation breakeven), the exact computation (Fisher's exact identity), and the condition under which it is computed. It also names the deliberate response field, distinguishing this tool from related curve and survey tools.

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?

There is an explicit 'Use when' section that directs agents to this tool for market-implied inflation pricing and points to brazil_focus_revisions for Focus survey expectations. A 'Do not use when' clause names brazil_yield_curve and brazil_real_curve as the correct alternatives for standalone curves.

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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