# ai.jawz/jawz (remote · jawz.ai)

Live macro data for AI agents: regime, conditions, liquidity, with input provenance. No signup.

- Trust score: 89/100 (high trust)
- Change this week: +3
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-09-20

## Components

- remote · `jawz.ai`: 89/100 (this document), [markdown](https://verifymcp.io/servers/ai-jawz-jawz/api-mcp.md), [page](https://verifymcp.io/servers/ai-jawz-jawz/api-mcp)

## Channel facts

- Endpoint: `https://jawz.ai/api/mcp`
- Transports: `streamable-http`
- Auth: `none`
- Version: `1.0.1`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-09-20.

- **Endpoint Security**: 94/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - Authorisation is enforced on tool calls, advertised via RFC 9728 protected-resource metadata. Discovery is public, which costs nothing: no tool can be invoked without a token.
  - HTTPS is enforced; there's no plaintext access path.
  - The HSTS (Strict-Transport-Security) header is present.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
  - The authorisation server offers only Dynamic Client Registration (RFC 7591), which MCP 2026-07-28 deprecated in favour of Client ID Metadata Documents.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 67/100
  - AI-judged instruction clarity (excellent).
  - Context-footprint check failed: tool/resource definitions use about 3853 tokens (~175/item across 22 items; 22 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 80/100
  - Stability observed for 24 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
  - Structured output schemas are declared (100% of tools); any adoption earns full credit.
- **Tool Safety**: 100/100
  - No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.
  - We read all 22 captured tool definition(s), and no name or description among them implies an irreversible operation.
  - An AI judge read all 22 captured unit(s) of tool text and found none that tries to manipulate the model reading it.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### How do I install the ai.jawz/jawz MCP server?

ai.jawz/jawz is a hosted endpoint at https://jawz.ai/api/mcp, so there is nothing to install locally. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

### Claude

```bash
claude mcp add --transport http ai-jawz-jawz 'https://jawz.ai/api/mcp'
```

### Cursor

```json
{
  "mcpServers": {
    "ai-jawz-jawz": {
      "url": "https://jawz.ai/api/mcp"
    }
  }
}
```

### VS Code

```json
{
  "servers": {
    "ai-jawz-jawz": {
      "type": "http",
      "url": "https://jawz.ai/api/mcp"
    }
  }
}
```

### Codex

```toml
[mcp_servers.ai-jawz-jawz]
url = "https://jawz.ai/api/mcp"
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "ai-jawz-jawz": {
      "type": "remote",
      "url": "https://jawz.ai/api/mcp",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add ai-jawz-jawz --url 'https://jawz.ai/api/mcp' --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  ai-jawz-jawz:
    url: "https://jawz.ai/api/mcp"
```

### Netclaw

```json
{
  "McpServers": {
    "ai-jawz-jawz": {
      "Transport": "http",
      "Url": "https://jawz.ai/api/mcp"
    }
  }
}
```

### Vellum

```bash
assistant mcp add ai-jawz-jawz -t streamable-http -u 'https://jawz.ai/api/mcp'
```

### Other

```json
{
  "mcpServers": {
    "ai-jawz-jawz": {
      "type": "http",
      "url": "https://jawz.ai/api/mcp"
    }
  }
}
```

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-09-20 (score 89, +1)

No change was recorded against any check on this day. Stability & Change Management went from 77 to 80. That category is still filling its 30-day observation window: 23 days of observed history at the previous scan, 24 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-18 (score 88, +1)

No change was recorded against any check on this day. Stability & Change Management went from 70 to 73. That category is still filling its 30-day observation window: 21 days of observed history at the previous scan, 22 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-16 (score 87, +1)

No change was recorded against any check on this day. Stability & Change Management went from 63 to 67. That category is still filling its 30-day observation window: 19 days of observed history at the previous scan, 20 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-15 (score 86, +47)

- [security improvement] Authorization: unverified → pass
- [security improvement] Injection markers: unverified → pass
- [functional regression] Schema quality: unverified → fail
- [functional regression] Schema quality: unverified → fail
- [functional improvement] Schema quality: unverified → excellent
- [functional improvement] Tool coverage: unverified → 100
- [functional] Stability: fail → 0.63
- [functional] This server's schema is too large to store in full, so we cannot compare its tools day to day

### 2026-09-14 (score 39, −47)

- [security regression] Authorization: pass → unverified
- [security regression] Tool safety: pass → unverified
- [security regression] Stability: 0.57 → fail
- [functional regression] Schema quality: excellent → unverified
- [functional regression] Schema quality: fail → unverified
- [functional regression] Schema quality: fail → unverified
- [functional regression] Tool coverage: 100 → unverified
- [functional] This server's schema is too large to store in full, so we cannot compare its tools day to day

### 2026-09-13 (score 86, +1)

No change was recorded against any check on this day. Stability & Change Management went from 53 to 57. That category is still filling its 30-day observation window: 16 days of observed history at the previous scan, 17 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-10 (score 85, +1)

No change was recorded against any check on this day. Stability & Change Management went from 43 to 47. That category is still filling its 30-day observation window: 13 days of observed history at the previous scan, 14 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-08 (score 84, +1)

No change was recorded against any check on this day. Stability & Change Management went from 37 to 40. That category is still filling its 30-day observation window: 11 days of observed history at the previous scan, 12 at this one. The score rises as the window fills, whether or not the server changes.

## MCP tools (22)

### `get_started` (~82 tokens)

Get started with Jawz

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.

Output parameters:

- `audience_note`
- `first_prompts`
- `how_to_go_deeper`
- `if_rumo_is_connected`
- `live_now`
- `memory_note`
- `terms_glossary`
- `what_this_is`

### `get_world_brief` (~124 tokens)

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.

Input parameters:

- `list_only` (boolean): Return only the list of available editions (slug, title, regimeLine, takeaway).
- `slug` (string): Edition to fetch, YYYY-MM-DD. Omit for the latest.

Output parameters:

- `available_editions`
- `byline`
- `canonical_url`
- `latest_url`
- `note`
- `regimeLine`
- `sections`
- `slug`
- `takeaway`
- `terms_glossary`
- `title`

### `get_prices` (~294 tokens)

Get prices

Live prices for one or more holdings, and the answer shows its work. Resolves raw tickers (stocks, ETFs, crypto, international listings) and returns price, value, and day change per symbol, plus per holding: what it resolved to (asset: type, exchange, ISIN, provider id), how surely (resolution: matched_by isin|qualified_symbol|bare_symbol, candidates, mismatch against the caller's hints), the native quote with its own time (quote), the FX rate actually applied, dated and sourced (fx), and the position value native and in the base currency. Optional per-holding hints — isin (equities/ETFs/funds), exchange, asset_class — narrow resolution; a hint that contradicts the result is reported, not silently overridden. Symbols that collide with tokenized-stock proxies on the crypto side (ALAB, LITE, NBIS, GLW, CBRS, DRAM, AIPO) resolve to the listed instrument, never the token; qualifying as NASDAQ:X / X.US does the same explicitly. base_currency defaults to USD; 'native' returns unconverted quotes.

Input parameters:

- `base_currency` (string|null): Currency for price, value and totalValue. Omit for USD (default). 'native' or null: no conversion — each line is stated in its quote currency and fx is null.
- `holdings` (array, required): Portfolio holdings to analyze

Output parameters:

- `holdings`
- `terms_glossary`
- `timestamp`
- `totalValue`
- `warnings`

### `get_macro_regime` (~90 tokens)

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.

Output parameters:

- `data`
- `generated_at`
- `pillars`
- `presentation`
- `staleness_flags`
- `status`
- `summary`
- `tables`
- `tool`
- `warnings`

### `get_regime_history` (~379 tokens)

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. Reading note, enforced by the artifact: the Liquidity column steps for two reasons that are NOT the market, and both are listed as first-class events in summary.liquidity_events — a g4↔g3 coverage flip (the whole PBoC component entering or leaving the sum: not a liquidity move at all), and a China-leg publication step (PBoC publishes monthly ~15d in arrears, so the day a new statement is recorded the column absorbs a whole month of change: real change, wrong date). Do not read that column as a trend without checking them; get_liquidity_history carries a constant-basis G3 series that is immune to both. 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 parameters:

- `interval` (string): Sample cadence (default weekly)
- `lookback_weeks` (number): How many weeks of history to cover (default 12, clamped 1–52)

Output parameters:

- `data`
- `generated_at`
- `presentation`
- `status`
- `summary`
- `tool`
- `warnings`

### `get_liquidity_history` (~396 tokens)

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, China-leg publication steps, 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. A China-leg publication step is a DIFFERENT event and is reported separately: PBoC publishes monthly ~15 days in arrears, so on the day a new statement is recorded the PBoC leg absorbs a whole month of balance-sheet change (plus FX) in one step — real change, but not change that happened on that date, and not the same thing as a coverage flip. 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 parameters:

- `interval` (string): Sample cadence (default weekly)
- `lookback_weeks` (number): How many weeks of history to cover (default 12, clamped 1–52)

Output parameters:

- `data`
- `generated_at`
- `presentation`
- `status`
- `summary`
- `tool`
- `warnings`

### `get_conditions_history` (~375 tokens)

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.

Input parameters:

- `interval` (string): Sample cadence (default weekly)
- `lookback_weeks` (number): How many weeks of history to cover (default 12, clamped 1–52)

Output parameters:

- `data`
- `generated_at`
- `presentation`
- `status`
- `summary`
- `tool`
- `warnings`

### `get_data_health` (~84 tokens)

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.

Output parameters:

- `any_stale`
- `any_unavailable`
- `as_of`
- `china_release_alert`
- `collector`
- `crons`
- `notices`
- `sources`
- `terms_glossary`
- `umich_release_alert`

### `get_financial_conditions` (~175 tokens)

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.

Input parameters:

- `summary_only` (boolean): If true, return composite + direction + one-line driver only

Output parameters:

- `as_of`
- `composite`
- `data_sources`
- `direction`
- `drivers`
- `pillars`
- `scope`
- `scope_note`
- `staleness_flags`
- `terms_glossary`

### `get_growth_indicators` (~131 tokens)

Get growth indicators

Growth pillar indicators: industrial production (ISM proxy), consumer sentiment, yield curve, initial claims, plus the Atlanta Fed GDPNow nowcast of the quarter in progress. GDPNow carries its own reference_quarter, the publication date of the estimate, and the previous estimate of the SAME quarter, so its direction of travel is readable; it is reported, not scored, so it does not move the classification. When no estimate is on file the block reports status: unavailable and says so in staleness_flags — not published, which is not the same as zero. Returns classification (green/yellow/red).

Output parameters:

- `as_of`
- `classification`
- `consumer_sentiment_umcsent`
- `data_sources`
- `gdpnow`
- `ism_manufacturing`
- `leading_indicators`
- `staleness_flags`
- `terms_glossary`

### `get_inflation_indicators` (~56 tokens)

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).

Output parameters:

- `as_of`
- `breakevens`
- `classification`
- `cpi`
- `data_sources`
- `pce`
- `staleness_flags`
- `terms_glossary`
- `wage_growth`

### `get_event_calendar` (~292 tokens)

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. COVERAGE: only fomc, cpi, nfp and pce have dates in this calendar. ecb, boj and boe are accepted for consistency with the consensus vocabulary but carry NO dates, so filtering to them returns an empty list and says so in staleness_flags — that is 'not tracked here', never 'none is scheduled'. Coverage also runs to a fixed last date; a window past it is flagged rather than silently short.

Input parameters:

- `event_types` (array): Filter by event types (default: fomc, cpi, nfp, pce). Only those four carry dates; ecb/boj/boe return empty with a staleness flag.
- `lookahead_days` (number): How many days forward to look (default 14)

Output parameters:

- `as_of`
- `data_sources`
- `events`
- `staleness_flags`
- `terms_glossary`

### `get_weekly_data_releases` (~226 tokens)

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.

Input parameters:

- `week_ending` (string): Week ending date (YYYY-MM-DD); defaults to last Friday

Output parameters:

- `as_of`
- `data_releases`
- `data_sources`
- `fed_h41_update`
- `policy_events`
- `staleness_flags`
- `terms_glossary`
- `week_ending`

### `get_etf_profile` (~173 tokens)

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.

Input parameters:

- `tickers` (array, required): ETF ticker symbols to look through (e.g. ['QQQ','VGT','SPY'])

Output parameters:

- `coverage`
- `data_sources`
- `guidance`
- `profiles`
- `requested`
- `terms_glossary`
- `unknown_tickers`

### `score_position_drift` (~288 tokens)

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.

Input parameters:

- `asset_class` (string, required): Asset class for fit-score lookup, drawn from the holding's actual exposure (Chapter 2 Mode 2.1) rather than from the current regime. A hybrid holding is scored per sleeve and weight-blended. Drives t…
- `conviction` (number): User-supplied conviction 1–10. Drives conviction_gap if a meaningful gap exists vs the regime fit score.
- `entry_date` (string, required): ISO date (YYYY-MM-DD) when the position was opened
- `symbol` (string, required): Ticker or instrument (e.g. AAPL, BTC, TLT)
- `symbol_type` (string): Price-API hint. Inferred from asset_class if omitted.
- `thesis` (string): Original thesis text. Echoed back word-for-word in review questions.

Output parameters:

- `as_of_date`
- `asset_class`
- `classification_warnings`
- `conviction_gap`
- `drift_label`
- `drift_score`
- `entry_date`
- `explanation`
- `fit_score_at_entry`
- `fit_score_now`
- `price_at_entry`
- `price_now`
- `regime_at_entry`
- `regime_now`
- `review_questions`
- `staleness_flags`
- `symbol`
- `terms_glossary`
- `total_return_pct`

### `get_drift_alerts` (~177 tokens)

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).

Input parameters:

- `portfolio` (array, required): Array of Decision Records — one per position the user wants scanned

Output parameters:

- `alerts`
- `as_of_date`
- `flagged_positions`
- `positions_without_records`
- `staleness_flags`
- `terms_glossary`
- `total_positions`

### `list_loops` (~54 tokens)

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.

Output parameters:

- `loops`
- `terms_glossary`

### `get_loop` (~61 tokens)

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.

Input parameters:

- `loop_slug` (string, required): Loop slug (e.g. 'jawz')

Output parameters:

- `author_name`
- `chapters`
- `description`
- `improvement_model`
- `methodology_statement`
- `price_cents`
- `pricing_model`
- `slug`
- `terms_glossary`
- `title`
- `version`
- `visibility`

### `get_chapter` (~77 tokens)

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.

Input parameters:

- `loop_slug` (string, required): Loop slug
- `node` (string, required): Chapter node

Output parameters:

- `author_name`
- `content`
- `loop_slug`
- `loop_title`
- `node`
- `terms_glossary`
- `title`
- `version`

### `run_chapter` (~117 tokens)

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.

Input parameters:

- `context` (string): Optional user context (portfolio details, ticker, question)
- `loop_slug` (string, required): Loop slug
- `node` (string, required): Chapter node

Output parameters:

- `author_name`
- `content_note`
- `framework_content`
- `invocation_id`
- `loop_slug`
- `loop_title`
- `node`
- `terms_glossary`

### `run_mode` (~113 tokens)

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.

Input parameters:

- `context` (string): Optional user context
- `loop_slug` (string, required): Loop slug
- `mode_slug` (string, required): Mode slug (e.g. 'equity-screen', 'regime-overlay')
- `node` (string, required): Chapter node

Output parameters:

- `author_name`
- `content_note`
- `framework_content`
- `invocation_id`
- `loop_slug`
- `loop_title`
- `mode_slug`
- `node`
- `terms_glossary`

### `get_china_liquidity` (~89 tokens)

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.

Output parameters:

- `row`
- `status`
- `terms_glossary`

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/ai-jawz-jawz/api-mcp#diagnostics

## Score history

- 2026-09-20: 89
- 2026-09-19: 88
- 2026-09-18: 88
- 2026-09-17: 87
- 2026-09-16: 87
- 2026-09-15: 86
- 2026-09-14: 39
- 2026-09-13: 86
- 2026-09-12: 85
- 2026-09-11: 85
- 2026-09-10: 85
- 2026-09-09: 84
- 2026-09-08: 84
- 2026-09-07: 83
- 2026-09-06: 83
- 2026-09-05: 82
- 2026-09-04: 82
- 2026-09-03: 81
- 2026-09-02: 81
- 2026-09-01: 81
- 2026-08-31: 80
- 2026-08-30: 80
- 2026-08-29: 80
- 2026-08-28: 79
- 2026-08-27: 79

## Common questions

### What is the ai.jawz/jawz MCP server?

ai.jawz/jawz is an MCP server listed in the public MCP registry as ai.jawz/jawz. Live macro data for AI agents: regime, conditions, liquidity, with input provenance. No signup. This page covers its hosted endpoint (https://jawz.ai/api/mcp).

### Is the ai.jawz/jawz MCP server safe to use?

ai.jawz/jawz scores 89 out of 100 on VerifyMCP. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.

### What tools does the ai.jawz/jawz MCP server expose?

ai.jawz/jawz exposes 22 tools: get_started, get_world_brief, get_prices, get_macro_regime, get_regime_history, and 17 more. Their descriptions and schemas cost roughly 3,853 tokens of context every time the server is loaded.

### Does the ai.jawz/jawz MCP server require authentication?

Yes. ai.jawz/jawz asked us for credentials when we connected, so you will need to authorise it in your MCP client before it can do anything.

### Is the ai.jawz/jawz MCP server still maintained?

ai.jawz/jawz is still listed as active in the MCP registry. We last reached this channel on 20 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.

## Links

- Remote endpoint: https://jawz.ai/api/mcp
- Website: https://jawz.ai/
- Changelog RSS feed: https://verifymcp.io/servers/ai-jawz-jawz/api-mcp.xml
- Changelog JSON feed: https://verifymcp.io/servers/ai-jawz-jawz/api-mcp.json
- HTML version of this page: https://verifymcp.io/servers/ai-jawz-jawz/api-mcp