brazil-macro-ai

brazil_yield_curve

Nominal Brazilian sovereign zero-coupon curve, bootstrapped from Tesouro Direto LTN (zero-coupon) and NTN-F (semi-annual coupon) offered-security quotes (method_id sovereign_zero_curve_bootstrap_v1).

curve_scope = "tesouro_direto_offered_securities": this is NOT an
institutional ANBIMA/Bloomberg ETTJ — it's derived from retail-offered
public securities, published as open data (ODbL license). Tenors
outside the curve's actual node coverage return
status="unsupported", never extrapolated.

Use when: you need Brazil's nominal risk-free curve shape or a
specific nominal zero rate.
Do not use when: you need the real (inflation-linked) curve (use
brazil_real_curve) or breakeven inflation (use
brazil_inflation_breakeven).

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 ($0.01/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 available, the description carries the full behavioral burden and does so thoroughly: it states the curve is retail-offered open data with ODbL license, not institutional, and that tenors outside node coverage return status='unsupported' and are never extrapolated. It also flags the required payment token, which is not visible in annotations.

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 dense but every sentence carries signal: definition, method, source caveat, unsupported-tenor behavior, usage guidance, and parameter details are all packed without redundancy. The Args section cleanly separates operational details from conceptual scope.

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?

Given no annotations and an output schema that handles return-value documentation, the description fully equips an agent to decide when to call, what parameters to provide, and what behavior to expect, including non-extrapolation and payment requirement. No critical gap remains for correct invocation.

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%, and the description compensates for the key operational parameters: as_of format/default, tenors allowed values/default, quote_side options, and payment_token requirement. The remaining schema fields (agent_id, session_id, discovery_source, synthetic_identity) are not explained, but they appear to be generic infrastructure parameters.

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 opens with a precise noun phrase — 'Nominal Brazilian sovereign zero-coupon curve' — and adds the bootstrapping source (Tesouro Direto LTN/NTN-F), method ID, and an explicit exclusion of institutional ANBIMA/Bloomberg curves. This clearly differentiates it from siblings like brazil_real_curve and brazil_inflation_breakeven.

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?

It includes direct 'Use when' and 'Do not use when' guidance, naming the specific sibling tools for the real curve and breakeven inflation. This eliminates ambiguity about when to choose this tool over alternatives.

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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