jawz

Get regime history

get_regime_history

Read-only

Macro regime read-history — a timeseries of how the regime and its drivers have moved over recent weeks. Returns one row per sample date (regime color, business-cycle quadrant, growth + inflation class, consumer sentiment, global liquidity, dominant risk, confidence) plus the transitions between them (e.g. SUMMER → FALL). Provides the observed history behind a 'what changed since …' question, rather than a comparison of two separate point reads. The response includes a 'presentation' object whose 'display_markdown' is a pre-formatted table; 'data.rows' and 'summary' carry the same values structured. Each row is provenance-tagged: 'observed' (captured live that day — true vintage) or 'reconstructed' (computed point-in-time from vintage data — each series as it was published on that date, so later revisions are excluded; depth-limited). Defaults to the last 12 weeks, weekly.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`interval`	No	Sample cadence (default weekly)
`lookback_weeks`	No	How many weeks of history to cover (default 12, clamped 1–52)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`data`	No
`tool`	No
`status`	No
`summary`	No
`warnings`	No
`generated_at`	No
`presentation`	No

TDQS

A4.5/5.0

Behavior5/5

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

The description substantially enriches the readOnlyHint annotation by explaining provenance tagging ('observed' vs 'reconstructed'), the point-in-time reconstruction semantics, exclusion of later revisions, depth limits, and the presentation object structure. This is exactly the kind of behavioral context an agent needs beyond 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is information-dense but every sentence contributes: purpose, output contents, use-case distinction, response shaping, provenance semantics, and defaults. It is front-loaded with the core purpose and never wanders into redundant filler.

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 two-parameter read-only tool with an output schema, the description covers all essential decision information: what the history represents, what each row contains, how provenance works, what the response structure looks like, and the default behavior. Nothing critical is missing for an agent to invoke it correctly.

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 input schema already fully documents both parameters, including enums, defaults, and clamping behavior, so the description adds little beyond restating the default cadence and lookback. Baseline 3 is appropriate because high schema coverage carries the parameter documentation burden.

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 clearly identifies the tool as a read-only history endpoint returning a timeseries of regime states and drivers, with specific fields listed. It distinguishes itself from a simple point-in-time comparison by explicitly framing the output as observed history behind a 'what changed since' question.

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 gives a clear use case: answering what changed over recent weeks via an observed historical sequence, and explicitly contrasts this with comparing two separate point reads. It does not name a specific sibling tool as the alternative, but the when-not guidance is clear enough for an agent to route appropriately.

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

A4.1/5.0

Disambiguation4/5

Most tools target clearly distinct resources—regime, liquidity, conditions, prices, ETF profiles, data health—and the three history tools are explicitly separated as price, flow, and judgment. The main ambiguity is get_chapter vs run_chapter, which both return chapter framework content and differ only in usage logging, though the descriptions call this out explicitly.

Naming Consistency4/5

The set follows a consistent snake_case verb_noun pattern: get_ for reads, list_ for enumeration, run_ for framework text, and score_ for position drift. The only wrinkle is run_chapter/get_chapter, where 'run' doesn't mean execution but rather 'return framework text and log usage,' making the verb semantics slightly less predictable.

Tool Count4/5

22 tools is on the heavy side but the set is organized into recognizable clusters: macro regime, liquidity/conditions, histories, portfolio drift, ETF/prices, loops/framework, and data health. Each tool appears to earn its place, so the count is slightly high but not bloated.

Completeness4/5

The surface is comprehensive for a read-and-analyze macro/portfolio server: current reads, historical timeseries, data freshness, event calendar, ETF look-through, drift scoring, and loop navigation are all covered. Minor gaps exist—no direct portfolio/position listing tool and non-US central-bank event dates are intentionally not tracked—but these are acknowledged and workable.

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