ai.jawz/jawz

Tools

• get_chapter

  Fetch the full content of a chapter by loop slug and node. Loops not marked open require entitlement; every published loop is currently open and free. Does not log an invocation; run_chapter is the variant that runs the framework and logs.

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• get_china_liquidity

  Read the latest Mako-curated PBoC balance-sheet publication (CNY trillions + source URL + as_of_month + note). Public read — suitable for surfacing directly when explaining the China leg of global liquidity. The same value is also embedded in get_financial_conditions.pillars.global_liquidity.components.china; this tool exists for direct/auditable access.

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• get_conditions_history

  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.

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• get_data_health

  Freshness status for every data source Jawz reads: per-source release date, latest observation date, expected cadence, age in days, and whether any source is stale or unavailable. Also reports whether the ingestion pipeline is keeping up, and carries plain-language notices when a scheduled update has not arrived. Applies when the reliability of a figure depends on how current its underlying data is.

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• get_drift_alerts

  Portfolio-wide drift scan. Iterates Decision Records, calls score_position_drift on each, returns flagged positions with severity + why_flagged + review_questions. Flag types: drift_severe (review_now), drift_meaningful (deteriorated), fit_low_structural (score ≤2 but unchanged since entry — low by design, not decay), fit_low_decayed (score ≤2 AND below entry fit — deteriorated after entry), conviction_gap (|gap| ≥3), thesis_undocumented (entry_date or thesis missing). Backs Chapter 4 Mode 4.3 (Thesis Status Sweep) as the portfolio-wide drift scan; flagged names feed a Mode 4.2 (Position Retrospective).

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• get_etf_profile

  ETF look-through for Chapter 2 Mode 2.2 (Concentration Check). Given ETF ticker(s), returns each fund's top holdings with weights so the AI can overlay them with the user's direct positions and surface hidden single-name concentration. Each profile carries source='live' (Vanguard API, fresh) or source='catalog' (dated snapshot — see as_of). Holdings are TOP-N only, so any true-exposure figure computed from this is a floor, not exact. Bond and commodity ETFs return no holdings by design. Tickers not in the catalog come back in unknown_tickers; their contents are not known to Jawz and no holdings are returned for them.

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• get_event_calendar

  Scheduled US macro events (FOMC + CPI + NFP + PCE) with dates and consensus. Consensus values are Mako-curated and arrive with provenance (source, source_url, published_at, age_days). An event with no curated consensus carries consensus: null, which means the value is not published rather than zero. Mode 1.2 covers the qualitative-scenario path for events whose consensus is not published. consensus_metrics_expected lists the metric keys a complete consensus would carry for that event type. Market-implied pricing (what_is_priced / CME FedWatch) is a separate quantity and remains deferred.

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• get_financial_conditions

  Full financial conditions read: global liquidity (Fed + ECB + BoJ central-bank balance sheets, FX-converted to USD; PBoC included when a fresh Mako-curated publication is available — basis 'g4' vs 'g3'), real yields, HY/IG credit spreads, DXY, VIX. US net liquidity is retained as a sub-component. Returns composite + direction + per-pillar classifications + drivers. The `scope` and `scope_note` fields tell you whether the read is G4 or G3, and the China sub-component carries Mako-curated provenance (as_of_month, source_url, note). Pass summary_only:true for the lightweight one-line read used in regime composition.

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• get_growth_indicators

  Growth pillar indicators: industrial production (ISM proxy), consumer sentiment, yield curve, initial claims. Returns classification (green/yellow/red).

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• get_inflation_indicators

  Inflation pillar indicators: headline + core CPI, headline + core PCE (YoY and MoM), 5Y/10Y breakevens, wage growth. Returns classification (supportive/neutral/headwind).

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• get_liquidity_history

  Global-liquidity read-history — a timeseries of central-bank liquidity over recent weeks. Returns one row per sample date with the full decomposition (Fed / ECB / BoJ / PBoC in USD trillions), the active basis (g4/g3/us_fallback), and the supportive/neutral/draining classification, plus coverage (basis) changes and classification changes as events. History companion to get_financial_conditions (Chapter 1 Mode 1.5 Global Liquidity Read). Reading note, enforced by the artifact: when the basis flips g4↔g3 (China PBoC publication freshness), the headline total moves by the ~$7T China component, which is a coverage change rather than a liquidity move. The window trend is therefore computed on constant G3 basis, and every basis flip is listed with an explicit note. 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.

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• get_loop

  Get metadata and chapter manifest for a loop by slug. Returns loop info and the list of chapter nodes with titles — no chapter content. All active loops are navigable by anyone.

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• get_macro_regime

  Get current macro regime (GREEN/YELLOW/RED) with business cycle positioning. The response carries the same values three ways: 'presentation.display_markdown' is a pre-formatted rendering with tables, 'tables' is the structured form of those tables, and 'data' holds the raw values. A 'provenance' block lists each input with its as-of date and age in days.

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• get_prices

  Live prices for one or more holdings. Resolves raw tickers (stocks, ETFs, crypto, international listings) and returns price, value, and day change per symbol. Takes symbol + quantity pairs. When a US stock or ETF ticker collides with a crypto coin sharing the symbol (e.g. AIPO, WIRE resolving to coins rather than the funds), qualifying it as NASDAQ:AIPO / NYSE:WIRE / AIPO.US selects the equity path.

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• get_regime_history

  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.

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• get_started

  Orientation for a new Jawz connection, and for questions about what Jawz is, what it covers, or how it is used. Returns a description of the Jawz Loop, example opening prompts, a one-line live market read drawn from the current data layer, a note on the plain-English glossary included in Jawz responses, and a link to the full guide.

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• get_weekly_data_releases

  Past week's data releases with H.4.1 net liquidity update. Anchored to Friday — most current when run Friday morning or later. Each release classifies actual vs consensus where BOTH are on file (both Mako-curated): `surprise` is hot / modestly hot / in-line / modestly cool / cool on the headline metric, `vs_consensus` is the same comparison stated neutrally as above/below/in-line, and `metrics` breaks it down per metric. hot/cool is DIRECTIONAL versus consensus, not a verdict — a hot CPI and a hot payrolls print mean opposite things for the same book. `surprise: "n/a"` means the comparison could not be made; `implication` names which half is missing. consensus_provenance.age_days is measured at week_ending, not at call time, so re-asking for an earlier week returns the same age it did the first time.

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• get_world_brief

  Read the Jawz World Brief — the weekly Mako-bylined market read published at jawz.ai/brief. Returns the latest edition by default, or a specific one by slug (YYYY-MM-DD). Every claim in a brief traces to a Jawz tool read from its week. Setting list_only:true returns the available editions without their content.

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• list_loops

  List all available Jawz loops. Returns loop metadata, access status, and which loop is currently active for this user. Every published loop is free and open. Deprecated loops you are entitled to are included with deprecated: true.

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• run_chapter

  Returns a chapter's published framework text: its modes, the questions each asks, and the output format it describes. The framework is reference material for the assistant to apply as it judges appropriate. Repeat calls return the same chapter content — the text is not regenerated per call. Each call appends one usage-counter row and returns a new invocation_id used for feedback.

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• run_mode

  Returns the published framework text for one named mode within a chapter, rather than the full chapter. Repeat calls return the same mode content — the text is not regenerated per call. Each call appends one usage-counter row and returns a new invocation_id used for feedback.

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• score_position_drift

  Score how a single position's regime fit has drifted since entry. Returns regime_at_entry, regime_now, fit_score_at_entry, fit_score_now, drift_score (now - entry), drift_label (improved/stable/deteriorated/review_now), explanation, and review_questions. No buy/sell recommendation — output is observational. Supports Chapter 4 Mode 4.2 (Position Retrospective) for single-name regime-fit review and Mode 4.3 (Thesis Status Sweep) for per-position drift across the book.

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