jawz

Get conditions history

get_conditions_history

Read-only

Financial-conditions read-history — a timeseries of how conditions have moved over recent weeks. Returns one row per sample date with the composite (loose/neutral/tight), direction (easing/stable/tightening), and every pillar's value + class: 10Y real yield, HY/IG credit spreads, DXY, VIX, global-liquidity class. The rate legs behind the real yield are exposed numerically too (dgs10_pct, t10yie_pct, t5yie_pct, t10y2y_pct), so a real-yield move can be read as nominal-led or breakeven-led rather than only as a fused number. Completes the Chapter 1 history trio with get_regime_history (the judgment) and get_liquidity_history (the flow) — this is the price of risk. Surfaces trajectory that a point read hides (e.g. HY spreads widening for six straight weeks while VIX stays calm), and grounds 'conditions are tightening' statements in observed pillar changes. Window deltas per pillar are in 'summary.deltas'; composite/direction/pillar classification changes are listed as events. The response includes a 'presentation' object whose 'display_markdown' is a pre-formatted table; 'data.rows' and 'summary' carry the same values structured. Rows are provenance-tagged 'observed' (live that day, true vintage) or 'reconstructed' (point-in-time from vintage data — each series as it was published on that date, ~4 months deep). 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

A4.4/5.0

Behavior5/5

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

Beyond the readOnlyHint=true and destructiveHint=false annotations, the description discloses key behavioral traits: observed vs reconstructed provenance, default lookback and cadence, delta and event summaries, pillar classifications, and how the response is structured with presentation, data.rows, and summary. This gives an agent a strong model of what the tool returns and how its data is sourced.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

The description is long but densely informative, front-loading the core purpose before adding output details, provenance, and defaults. Each segment adds value for an agent deciding to call the tool and parse the response. It could be tightened slightly, but the structure is logical and there is little redundancy.

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 the tool's read-only, two-parameter simplicity and the presence of an output schema, the description is more than complete. It explains the provenance model, the distinction among the history trio, the output structure, and the defaults. An agent has all necessary context to select and invoke this tool 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?

Schema description coverage is 100%, so the schema already fully documents interval and lookback_weeks. The description mostly restates the defaults ('Default to the last 12 weeks, weekly') rather than adding meaning beyond the schema. It does clarify the shape of the returned rows, but that is output behavior more than parameter semantics.

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 uses a specific verb-plus-resource framing: 'Financial-conditions read-history — a timeseries of how conditions have moved over recent weeks.' It clearly distinguishes this tool from get_regime_history and get_liquidity_history by naming them and stating 'this is the price of risk.' This makes the tool's scope immediately identifiable.

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 provides clear context for when to use the tool, noting it 'surfaces trajectory that a point read hides' and that it 'grounds conditions are tightening statements in observed pillar changes.' It names sibling tools and differentiates their roles, though it stops short of explicit when-not-to-use instructions or a direct conditional routing statement.

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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