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indicator_history

Read-onlyIdempotent

Returns the full published history of a Brazilian reference series, so you can put macro context next to a bond's own rate history without leaving this server. Every rate is a decimal-fraction string, the same convention as every other tool here.

Available indicator values:

  • selic-target — the meta Selic set by the Copom, from the official Banco Central rate history. Annual (unit: 'annual-decimal-fraction', '0.1425' = 14.25% a.a.), so it compares with price_history_series directly.

  • ipca-monthly — the IPCA measured and published by the IBGE (read from Banco Central SGS series 433), every month since January 1980. Monthly (unit: 'monthly-decimal-fraction', '0.0007' = 0.07% a.m.).

ALWAYS read unit before comparing series or charting them together: an IPCA month is not an annual rate, and treating '0.0044' as 0.44% a.a. instead of 0.44% a.m. is off by more than a factor of ten. To annualize a month, compound it; to get a 12-month accumulated IPCA, compound the twelve monthly readings — do not add them.

For selic-target this is EVERY decision since the regime began in March 1999, including the meetings that held the rate (outcome: 'hold', changeBps: 0). Read outcome to tell them apart: up, down, hold, or start for the single opening entry. Count only up/down when the user asks how many times the rate actually moved.

Two selic-target fields are easy to misread. meetingNumber is NOT unique: until 2003 a meeting could set a bias ('alta'/'baixa') authorizing the Banco Central president to move the target before the next meeting, alone, and those decisions carry monocratic: true under the authorizing meeting's number — effectiveDate is the key. And effectiveRate is the Selic actually realized over the period, a different quantity from the target in rate; it is null while a period is still in force.

For ipca-monthly, referenceMonth is the month the index MEASURES, not the month it was released — the IPCA of a month comes out around the 10th of the next one, so the most recent entry is normally the previous month — or the one before it, in the first days of a month, while the release is still pending. These are realized readings only; this server holds no IPCA forecast, and the projections that go into pricing an IPCA+ bond are a different thing, not exposed here. Readings before July 1994 are from the hyperinflation era and are genuinely that large (82.39% in March 1990).

Neither series by itself sets the rate or price of any Treasury bond, and a move in the same window is not evidence that one caused the other. These are factual series, not a forecast, a market interpretation, or a recommendation.

Returns { series, unit, source: { id, publisher }, items }. For selic-target, items is [{ decisionDate, effectiveDate, rate, previousRate, changeBps, outcome, meetingNumber, extraordinary, monocratic, bias, effectiveRate }] ascending by effectiveDate. For ipca-monthly, items is [{ referenceMonth, rate }] ascending by referenceMonth, one entry per month with no gaps.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
indicatorYesWhich reference series to return. `selic-target` is the Selic target rate set by the Copom (Banco Central), annual. `ipca-monthly` is the IPCA published by the IBGE, monthly — the realized index, not the projection used in pricing.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already mark this as read-only and idempotent, but the description goes far beyond them. It explains decimal-fraction string conventions, unit differences, outcome semantics, monocratic decisions, effective versus target rates, referenceMonth timing, hyperinflation-era magnitudes, and the limitation that these are realized facts, not forecasts or recommendations. This is rich behavioral disclosure.

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?

This is a longer description, but every sentence earns its place. It uses bullets and bolded emphasis to structure key distinctions, returns format warnings upfront, and explained without fluff. Despite its length, it remains readable because the unit caveat, outcome semantics, and output shape are all clearly organized.

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 description is complete for a single parameter read-only tool: it defines the available inputs, output structure, return values, unit semantics, common misinterpretations, and historical caveats given the limited input schema and high complexity of the data. An agent can confidently choose the correct indicator and interpret the numeric response without further context.

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

Parameters5/5

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

Although the schema already documents the indicator parameter with an enum, the description adds substantial meaning beyond it: annual versus monthly semantics, exact sources, date ranges, comparison safety, caveats about compounding, and interpretation guidance. It fully covers the two possible values in a useful way and even explains how to handle edge cases.

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 action and resource: returning the full published history of a Brazilian reference series. It explicitly distinguishes this from bond price tools by explaining it supplies macro context alongside a bond's rate history. The mention of comparing with price_history_series directly further separates it from siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly frames when this context is relevant and warns against using the series as forecasts or causal evidence. It does not explicitly enumerate sibling alternatives or state 'use X for bond pricing instead,' but the context and exclusions are strong enough for an agent to route correctly.

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

A4.6/5.0
Disambiguation4/5

Most tools have clearly distinct purposes: catalog_list for discovery, live_quotes_lookup for real-time quotes, price_history_lookup for specific-date lookups, price_history_series for time series, price_history_ranking for cross-bond comparisons, and simulate_* for simulations. The overlap among the four quote/history tools is real but mitigated by detailed descriptions clarifying when each should be used.

Naming Consistency4/5

All tool names use lowercase snake_case with descriptive words, but the pattern is slightly mixed: catalog_list, live_quotes_lookup, and price_history_* follow an object+verb structure, while simulate_bond and simulate_redemption follow a verb+object structure. Though not perfectly uniform, the naming remains predictable and readable.

Tool Count5/5

With 8 tools, the server is well-scoped for its domain—covering catalog discovery, live and historical quotes, rankings, indicators, and simulations. No tool feels redundant or extraneous, and the count is well within the ideal 3-15 range.

Completeness4/5

The tool surface covers the core lifecycle: discover bonds, fetch live/historical rates, simulate pricing, and simulate redemption with tax details. A minor gap is that indicator_history currently supports only 'selic-target' despite implying broader coverage, but this does not undermine the server's primary purpose.

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