FinTurb Analytics
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get_absorption_ratio open 1h ago
How tightly today's asset classes are moving together — a measure of Financial Fragility. Returns Financial Fragility (Absorption Ratio, AR) — the share of cross-asset return variance explained by the top two principal components, i.e. how much of the market is being driven by a single dominant factor. High values mean diversification is breaking down and a shock in one asset spills into the rest of the system. Returns: - Three-tier Financial Fragility alert read across independent rolling windows: • 30-day Watch (Absorption Ratio above the 75th percentile) • 60-day Warning (above the 90th percentile) • 90-day Crisis (above the 95th percentile) - Per tier: the raw Absorption Ratio level, its percentile rank, whether the tier is currently triggered, and the threshold - Overall alert level (none / watch / warning / crisis), number of active tiers, and a plain-English coupling classification (Low / Below Average / Above Average / High) Call this when the user asks: "is the market crowded", "are asset classes moving together", "is diversification working", "is there contagion risk", "systemic risk", "are we in a fragile market", "PCA stress", "Absorption Ratio", "asset correlation breakdown", "how coupled are markets right now". Updated daily ~00:30 UTC. For per-asset PC1/PC2 eigenvector loadings across the three windows use `get_pc_loadings_history`. For the raw AR time-series tail only use `get_fragility_loadings`.
{ "type": "object", "title": "get_absorption_ratioArguments", "properties": {} }arguments 5 linesagent_analyst_options_flow unknown never probed
Agentic firm-simulation TOOL form — options-flow analyst — skew, term structure, parity check (input report, not actionable scoring). Tool-callable wrapper around the matching @mcp.prompt of the same role. Use this from MCP hosts that cannot invoke prompts programmatically (e.g. claude.ai web tool_use blocks). The methodology lives at the resource `finturb://skills/agents/analyst_options_flow` and MUST be read before producing output. Arguments: ticker (optional; defaults to 'Market'), upstream_reports (optional; downstream-chain context), as_of (optional; ISO date for point-in-time runs).
{ "type": "object", "title": "tool_agent_analyst_options_flowArguments", "properties": { "as_of": { "type": "string", "title": "As Of", "default": "" }, "ticker": { "type": "string", "title": "Ticker", "default": "" }, "upstream_reports": { "type": "string", "title": "Upstream Reports", "default": "" } } }arguments 21 linesagent_analyst_geopolitical unknown never probed
Agentic firm-simulation TOOL form — geopolitical analyst — GDELT media tone, regional divergence, alert surface; FinTurb-native. Tool-callable wrapper around the matching @mcp.prompt of the same role. Use this from MCP hosts that cannot invoke prompts programmatically (e.g. claude.ai web tool_use blocks). The methodology lives at the resource `finturb://skills/agents/analyst_geopolitical` and MUST be read before producing output. Arguments: ticker (optional; defaults to 'Market'), upstream_reports (optional; downstream-chain context), as_of (optional; ISO date for point-in-time runs).
{ "type": "object", "title": "tool_agent_analyst_geopoliticalArguments", "properties": { "as_of": { "type": "string", "title": "As Of", "default": "" }, "ticker": { "type": "string", "title": "Ticker", "default": "" }, "upstream_reports": { "type": "string", "title": "Upstream Reports", "default": "" } } }arguments 21 linesagent_analyst_fundamentals unknown never probed
Agentic firm-simulation TOOL form — fundamentals analyst — valuation, growth, balance sheet input report. Tool-callable wrapper around the matching @mcp.prompt of the same role. Use this from MCP hosts that cannot invoke prompts programmatically (e.g. claude.ai web tool_use blocks). The methodology lives at the resource `finturb://skills/agents/analyst_fundamentals` and MUST be read before producing output. Arguments: ticker (optional; defaults to 'Market'), upstream_reports (optional; downstream-chain context), as_of (optional; ISO date for point-in-time runs).
{ "type": "object", "title": "tool_agent_analyst_fundamentalsArguments", "properties": { "as_of": { "type": "string", "title": "As Of", "default": "" }, "ticker": { "type": "string", "title": "Ticker", "default": "" }, "upstream_reports": { "type": "string", "title": "Upstream Reports", "default": "" } } }arguments 21 linesagent_analyst_technical unknown never probed
Agentic firm-simulation TOOL form — technical analyst — get_ticker_metrics + RATO + stat-arb environment. Tool-callable wrapper around the matching @mcp.prompt of the same role. Use this from MCP hosts that cannot invoke prompts programmatically (e.g. claude.ai web tool_use blocks). The methodology lives at the resource `finturb://skills/agents/analyst_technical` and MUST be read before producing output. Arguments: ticker (optional; defaults to 'Market'), upstream_reports (optional; downstream-chain context), as_of (optional; ISO date for point-in-time runs).
{ "type": "object", "title": "tool_agent_analyst_technicalArguments", "properties": { "as_of": { "type": "string", "title": "As Of", "default": "" }, "ticker": { "type": "string", "title": "Ticker", "default": "" }, "upstream_reports": { "type": "string", "title": "Upstream Reports", "default": "" } } }arguments 21 linesagent_analyst_news unknown never probed
Agentic firm-simulation TOOL form — news analyst — ticker headlines, materiality ranking, tone convergence. Tool-callable wrapper around the matching @mcp.prompt of the same role. Use this from MCP hosts that cannot invoke prompts programmatically (e.g. claude.ai web tool_use blocks). The methodology lives at the resource `finturb://skills/agents/analyst_news` and MUST be read before producing output. Arguments: ticker (optional; defaults to 'Market'), upstream_reports (optional; downstream-chain context), as_of (optional; ISO date for point-in-time runs).
{ "type": "object", "title": "tool_agent_analyst_newsArguments", "properties": { "as_of": { "type": "string", "title": "As Of", "default": "" }, "ticker": { "type": "string", "title": "Ticker", "default": "" }, "upstream_reports": { "type": "string", "title": "Upstream Reports", "default": "" } } }arguments 21 linesagent_analyst_sentiment unknown never probed
Agentic firm-simulation TOOL form — social-sentiment analyst — retail crowding, divergence vs formal news. Tool-callable wrapper around the matching @mcp.prompt of the same role. Use this from MCP hosts that cannot invoke prompts programmatically (e.g. claude.ai web tool_use blocks). The methodology lives at the resource `finturb://skills/agents/analyst_sentiment` and MUST be read before producing output. Arguments: ticker (optional; defaults to 'Market'), upstream_reports (optional; downstream-chain context), as_of (optional; ISO date for point-in-time runs).
{ "type": "object", "title": "tool_agent_analyst_sentimentArguments", "properties": { "as_of": { "type": "string", "title": "As Of", "default": "" }, "ticker": { "type": "string", "title": "Ticker", "default": "" }, "upstream_reports": { "type": "string", "title": "Upstream Reports", "default": "" } } }arguments 21 linesget_risk_score unknown never probed
Today's plain-English read on overall market risk and what regime markets are in. Returns: - **Signal Strategist composite** — the canonical headline figure: composite_strategist (0-100), regime_strategist (Normal / Elevated / Crisis), quadrant_strategist (Constructive / Trending / Stress / Systemic Risk), gate_strategist (Fragility Gate active or dormant), turb_pct (Financial Turbulence percentile), frag_pct (Financial Fragility percentile). Calibrated on full 2008+ history. Lead the narrative with these figures. - **Legacy 3-way composite Risk Score** (0–100) and 4-tier Investment Regime label (normal / elevated / stress / crisis). Kept for parallel comparison during the parallel evaluation period; will be retired afterwards. - Liquidity-adjusted Risk Score (the 4-way score) and its own regime label, plus a decomposition (base score + funding-conditions boost + fragility-meets-tight-liquidity kicker) - Financial Fragility (Absorption Ratio, AR) percentile — systemic coupling between asset classes - Financial Turbulence (Mahalanobis Distance, MD) percentile — how unusual today's cross-asset price moves are - GDELT media tone (global news sentiment z-score) - Joint stress flags including fragile-shock and the 4-way joint signals (fragility × tight liquidity, tight Private Sector Liquidity, tight Policy Liquidity Index) - Global Liquidity Index, Policy Liquidity Index, Private Sector Liquidity, Cross-Border Flows Index sub-block Call this when the user asks about: "how risky are markets right now", "today's market regime", "should I be worried about a sell-off", "are we in risk-on or risk-off", "what's the overall risk picture", "is the market in stress", "what is the current regime", or anything asking for a single headline read on present-day market conditions. Updated daily ~03:40 UTC. Do NOT use for historical trends — use `get_risk_history` instead. Chain with `get_signal_strategist` for the fuller dashboard-grade read.
{ "type": "object", "title": "get_risk_scoreArguments", "properties": {} }arguments 5 linesget_strategist_composite unknown never probed
Signal Strategist composite — the canonical headline read on today's market regime, with no legacy fields in the response. Returns: - composite_strategist (0-100) — Turbulence-anchored composite (0.6 × Financial Turbulence pct + 0.4 × Financial Fragility pct, +5 bonus when both signals are in their top quartile, capped at 100) - regime_strategist — Normal (<25), Elevated (25-75), or Crisis (>75) - quadrant_strategist — Constructive / Trending / Stress / Systemic Risk - gate_strategist — boolean (1/0); 1 means the Fragility Gate is active - stance_strategist — Risk Stance (Neutral / Harvest / De-Risk / Hedge) from the strict top-quartile quadrant; the actionable read - turb_pct, frag_pct — underlying signal percentiles - data_as_of — date the snapshot was calibrated through Call this when the user wants the Signal Strategist read without the legacy 3-way / 4-way comparison clutter — for example: "what's today's Signal Strategist composite", "current quadrant", "is the Fragility Gate active". For the parallel comparison against the legacy composites, use `get_risk_score` instead. Updated daily. Updated daily ~03:53 UTC by build_risk_synthesizer.py.
{ "type": "object", "title": "get_strategist_compositeArguments", "properties": {} }arguments 5 linesget_quadrant_state unknown never probed
Regime Quadrant — joint position of Financial Turbulence and Financial Fragility. Useful when the user is specifically asking about the four-archetype regime grid (Constructive / Trending / Stress / Systemic Risk) rather than the headline composite score. Returns: - quadrant — current quadrant label - turb_pct, frag_pct — coordinates inside the grid - description — one-line plain-English read of the quadrant's meaning - is_corner_cell — True if turbulence AND fragility are BOTH in their top quartile (Systemic Risk corner; historically a 3.4× tail-risk amplifier) Call this when the user asks: "what quadrant are we in", "is the Systemic Risk corner active", "how concentrated is the market right now", or anything explicitly about the four-quadrant regime grid. Updated daily ~03:53 UTC.
{ "type": "object", "title": "get_quadrant_stateArguments", "properties": {} }arguments 5 linesget_stat_arb_summary unknown never probed
A quick headline of which assets are at mean-reversion extremes today. Returns: - Counts of how many tickers are flagged oversold versus overbought after the upstream stat-arb (statistical arbitrage / mean-reversion) screen - Top 5 oversold names (potential bounce candidates) ranked by stat-arb opportunity score - Top 5 overbought names (potential pullback candidates) ranked by stat-arb opportunity score Call this when the user asks: "what's at extremes today", "any stat-arb ideas right now", "top mean-reversion names", "headline stat-arb read", "give me the leaders both ways", "where's the action in stat-arb". For the full ranked lists drill into `get_oversold_opportunities` or `get_overbought_opportunities`; for a single named ticker use `get_ticker_metrics`.
{ "type": "object", "title": "get_stat_arb_summaryArguments", "properties": {} }arguments 5 linesget_options_chain unknown never probed
Fetch a structured option chain for a US-listed equity or ETF from Yahoo Finance — spot, risk-free rate, dividend info, and per-expiry calls + puts with bid/ask/mid/IV/volume/open-interest. Returns: - symbol, spot, fetch_time, risk_free_rate - dividend_yield, next_dividend, next_ex_date (when supplied) - is_etf, sector - expiries[]: each with date, dte, occ_adjusted, adjustment_amount (if any), plus `calls` and `puts` arrays of row dicts. Each row carries: strike, bid, ask, mid, last, volume, open_interest, iv, delta, gamma, theta, vega, in_the_money, contract_size, stale_quote. - Strikes outside spot ± strike_range_pct% are filtered out by default to keep responses within MCP token bounds. Inputs: - ticker (required): equity / ETF symbol, e.g. 'NVDA', 'AAPL', 'MNST', 'SPY'. - max_expiries (optional, default 2, max 6): nearest N expiration dates. - strike_range_pct (optional, default 25.0): keep strikes within ±this % of spot. Set to 100 (or higher) for the full chain — useful for butterfly / box-spread sweeps, but watch the response size. Call this when the user asks: "scan [ticker] options for mispricings", "pull the option chain on [ticker]", "is the [ticker] vol surface arbed", "options arbitrage check on [ticker]", "show me the [ticker] calendar spreads", "what's the IV smile on [ticker]". Always call this BEFORE invoking the options_mispricing_scan skill so the skill has structured chain data to score. Notes: - Uses yfinance under the hood — the same data path the FinTurb daily options scanner uses in production. ETF and high-liquidity equities generally return complete chains; thin micro-caps may return sparse or missing IV / Greek fields. - Greeks (delta, gamma, theta, vega) may be NaN where yfinance does not supply them. The 7-test mispricing scan does not strictly require all Greeks — bid / ask / mid / IV / volume / OI are sufficient. - OCC-adjusted contracts are flagged at the expiry level so the skill's parity tests can adjust accordingly.
{ "type": "object", "title": "get_options_chainArguments", "required": [ "ticker" ], "properties": { "ticker": { "type": "string", "title": "Ticker" }, "max_expiries": { "type": "integer", "title": "Max Expiries", "default": 2 }, "strike_range_pct": { "type": "number", "title": "Strike Range Pct", "default": 25 } } }arguments 23 linesagent_analyst_macro_regime unknown never probed
Agentic firm-simulation TOOL form — macro regime analyst — Financial Turbulence + Financial Fragility + Signal Strategist composite + Regime Quadrant; FinTurb-native. Tool-callable wrapper around the matching @mcp.prompt of the same role. Use this from MCP hosts that cannot invoke prompts programmatically (e.g. claude.ai web tool_use blocks). The methodology lives at the resource `finturb://skills/agents/analyst_macro_regime` and MUST be read before producing output. Arguments: ticker (optional; defaults to 'Market'), upstream_reports (optional; downstream-chain context), as_of (optional; ISO date for point-in-time runs).
{ "type": "object", "title": "tool_agent_analyst_macro_regimeArguments", "properties": { "as_of": { "type": "string", "title": "As Of", "default": "" }, "ticker": { "type": "string", "title": "Ticker", "default": "" }, "upstream_reports": { "type": "string", "title": "Upstream Reports", "default": "" } } }arguments 21 linesagent_analyst_liquidity unknown never probed
Agentic firm-simulation TOOL form — liquidity analyst — Global / Policy / Private Sector Liquidity + Cross-Border Flows; FinTurb-native. Tool-callable wrapper around the matching @mcp.prompt of the same role. Use this from MCP hosts that cannot invoke prompts programmatically (e.g. claude.ai web tool_use blocks). The methodology lives at the resource `finturb://skills/agents/analyst_liquidity` and MUST be read before producing output. Arguments: ticker (optional; defaults to 'Market'), upstream_reports (optional; downstream-chain context), as_of (optional; ISO date for point-in-time runs).
{ "type": "object", "title": "tool_agent_analyst_liquidityArguments", "properties": { "as_of": { "type": "string", "title": "As Of", "default": "" }, "ticker": { "type": "string", "title": "Ticker", "default": "" }, "upstream_reports": { "type": "string", "title": "Upstream Reports", "default": "" } } }arguments 21 linesget_firm_bundle unknown never probed
Run a full firm simulation in ONE fetch - returns the complete 10-stage run-book PLUS all 21 agent methodologies (9 analysts, catalyst graph, transmission analyst, fragility gate, bull/bear researchers, research manager, three-way risk panel, trader, math validator, PM) in a single payload. PREFER this over calling firm_simulation + individual agent_* tools whenever executing the whole pipeline: it replaces ~20 static-text fetches and prevents the tool-call-quota aborts seen on claude.ai web. In bundle mode do NOT call the agent_* wrapper tools at all - produce each role's report directly from its embedded methodology; only get_* data tools, list_memos/read_memo, web search, and finalize_memo/finalize_audit are live tool calls. Args: ticker (required), mandate (optional, default 'default-v1'), as_of (optional ISO date).
{ "type": "object", "title": "tool_get_firm_bundleArguments", "required": [ "ticker" ], "properties": { "as_of": { "type": "string", "title": "As Of", "default": "" }, "ticker": { "type": "string", "title": "Ticker" }, "mandate": { "type": "string", "title": "Mandate", "default": "default-v1" } } }arguments 23 linesget_phillips_prediction unknown never probed
Decision-tree next-month Core CPI prediction (inflation-inertia model). Walks the parsed tree leaves (CPI_Core_lag_1 carries ~99.9% of the feature importance) to find the predicted next-month Core CPI from the latest reading as lag-1 input. Returns: - Next-month forecast: predicted Core CPI using current as lag-1 - Latest backtest: previous prediction vs realised, error in pp - Model performance: Test RMSE, R-squared - Tree leaves (every constraint band + emitted value) Call this when the user asks "what does the model predict", "where will Core CPI be next month", "what's the inflation forecast", "what does the decision tree say".
{ "type": "object", "title": "get_phillips_predictionArguments", "properties": {} }arguments 5 linesget_inflation_history unknown never probed
Trailing time series for any inflation-related series we track. Args: series: one of: CPI_Core_Sticky | CPI_Headline_YoY | CPI_Core_YoY | PCE_Headline_YoY | PCE_Core_YoY | UMich_1Y | BE_5Y | BE_10Y | FWD_5Y5Y | EXPINF_1Y | Gap_Unemployment | Gap_Output | Gap_LFP months: trailing number of monthly observations to return (default 36). Call this when the user asks "show me the history of X", "what has 5Y breakeven been doing", "give me last 3 years of Core CPI", "plot LFP gap over time".
{ "type": "object", "title": "get_inflation_historyArguments", "properties": { "months": { "type": "integer", "title": "Months", "default": 36 }, "series": { "type": "string", "title": "Series", "default": "CPI_Core_Sticky" } } }arguments 16 linesget_phillips_curve_evolution unknown never probed
How the Phillips Curve has evolved across decades and what it implies for the potency of monetary policy. For each decade from the 1970s through the current decade, runs a simple OLS regression of Core CPI on the LFP Gap (our preferred labor-slack measure) and returns the slope, intercept, R-squared, t-stat, p-value, and a regime label. Slope regime interpretation: - STEEP_POSITIVE (slope >= 1.5, p < 0.10): classic textbook Phillips Curve - labor tightness translates strongly into inflation. Monetary policy that cools the labor market is highly effective. - MODERATE_POSITIVE (0.5 <= slope < 1.5, p < 0.10): partial responsiveness. Policy effective with longer lags. - FLAT (|slope| < 0.5 or p >= 0.10): inflation is anchored by other factors (expectations, supply curves). Labor channel of monetary policy is muted. - INVERTED (slope <= -0.5, p < 0.10): supply-side regime where labor and inflation decouple - e.g. the 2020s pandemic where LFP fell while inflation spiked. Call this when the user asks "how has the Phillips Curve changed over time", "is the unemployment-inflation relationship still there", "how does monetary policy work in different inflation regimes", "explain the Phillips Curve evolution".
{ "type": "object", "title": "get_phillips_curve_evolutionArguments", "properties": {} }arguments 5 linesget_risk_history unknown never probed
How market risk and the Investment Regime have evolved over the recent past. Returns: - **Signal Strategist read for today** (headline overlay) — composite_strategist (0-100), regime_strategist (Normal / Elevated / Crisis), quadrant_strategist (Constructive / Trending / Stress / Systemic Risk), gate_strategist (Fragility Gate active or dormant). Lead the narrative with these figures. - **Legacy 4-tier daily history** — daily 3-way composite Risk Score (0-100) for the last N days (default 90) with regime labels on the retiring 4-tier ladder (normal / elevated / stress / crisis) and a regime-duration counter. Kept for the parallel evaluation period; the legacy daily label can diverge from today's strategist read when the regime is shifting. Call this when the user asks: "how has risk changed over the last month", "compared to last week", "is the regime getting worse", "show me the trend", "how long have we been in stress", "regime history", "track the risk over time", "did things improve since last week". Strategist historical regime values are not yet persisted at this endpoint — only today's strategist read is overlaid. For a single-day point-in-time read use `get_risk_score` or `get_strategist_composite`.
{ "type": "object", "title": "get_risk_historyArguments", "properties": { "days": { "type": "integer", "title": "Days", "default": 90 } } }arguments 11 linesget_conditional_returns unknown never probed
How major asset classes have historically performed in each Investment Regime. Returns: - Mean forward return, probability of a negative return, and CVaR (Conditional Value-at-Risk — the average loss in the worst 5% of outcomes) for each core asset (SPY, HYG, EMB, GSG, GLD, VNQ, ACWX) bucketed by Investment Regime - Horizon parameter: '5d' (one trading week) or '21d' (one trading month) Call this when the user asks: "what usually happens to stocks in a stress regime", "what's the expected loss if we're in crisis", "how does gold do in risk-off", "what's the historical drawdown for high yield in elevated regime", "should I expect SPY to drop", "what's the worst-case in this regime". Pair with `get_risk_score` so the user knows the current Investment Regime before reading the conditional table.
{ "type": "object", "title": "get_conditional_returnsArguments", "properties": { "horizon": { "type": "string", "title": "Horizon", "default": "5d" } } }arguments 11 linesget_interaction_table unknown never probed
How an asset has performed historically when Fragility, Turbulence, and Sentiment all line up (or don't). Returns: - 2×2×2 historical conditional return table for one asset, broken down by Financial Fragility (Absorption Ratio, AR) high/low, Financial Turbulence (Mahalanobis Distance, MD) high/low, and GDELT media tone positive/negative - Available assets: SPY, BTC_USD, GLD, HYG, or 'all' for the aggregate cross-asset table Call this when the user asks: "how does SPY do when everything is bad at once", "what happens to gold when systemic risk and turbulence both spike", "show me the joint conditioning", "compound conditioning table", "all signals lining up scenario", or wants to drill into one asset's behaviour across the full eight-cell stress map. For single-signal regime-conditioned tables use `get_conditional_returns`.
{ "type": "object", "title": "get_interaction_tableArguments", "properties": { "asset": { "type": "string", "title": "Asset", "default": "SPY" } } }arguments 11 linesget_turbulence_score unknown never probed
How unusual today's cross-asset price moves are versus their historical pattern. Returns: - Financial Turbulence (Mahalanobis Distance, MD) daily reading: raw value, expanding-history percentile, quartile (1–4), and regime label (Risk-On / Neutral / Financial Turbulence) - 10-day rolling Financial Turbulence reading on the same scale — smooths out single-day noise - Master daily and 10-day percentiles across the unweighted 13-asset model Call this when the user asks: "how turbulent are markets", "are today's moves unusual", "is there cross-asset stress", "what is the turbulence reading", "how volatile is the market", "is volatility elevated", "how big are today's market moves historically", "is this a normal day or a statistically odd one". Pair with `get_signal_dates` for historical analogues, and with `get_absorption_ratio` to separate "today is unusual" from "today is unusual AND the system is fragile".
{ "type": "object", "title": "get_turbulence_scoreArguments", "properties": {} }arguments 5 linesget_transition_probabilities unknown never probed
The base-rate odds of staying in or moving out of today's Financial Turbulence state. Returns: - Markov transition matrices for the Financial Turbulence (Mahalanobis Distance, MD) regime — both the daily reading and the smoother 10-day rolling reading - For each turbulence state and how long it has lasted, the probability of remaining versus transitioning to each other state - A note that probabilities within ±3 percentage points of each other are statistically indistinguishable Call this when the user asks: "how long does this regime last", "what are the odds we stay in stress", "what's the base-rate path from here", "probability we move out of risk-off", "is this turbulence likely to persist", "how sticky is the current market state". Pair with `get_turbulence_score` so the user knows which state today sits in before reading the transition odds.
{ "type": "object", "title": "get_transition_probabilitiesArguments", "properties": {} }arguments 5 linesget_signal_dates unknown never probed
Historical episodes when Financial Turbulence flagged a bullish or bearish setup, and what happened next. Returns: - Type-A bearish (risk-off) signal dates: when Financial Turbulence (Mahalanobis Distance, MD) flagged a likely sell-off setup, with the percentile reading, signal strength, what asset prices did afterwards, and whether the call worked out - Type-B bullish (risk-on) signal dates with the same fields - signal_type parameter: 'A', 'B', or 'both' Call this when the user asks: "what happened the last time markets looked like this", "show me historical analogues", "hit rate of the bearish signal", "did the bullish signal work in the past", "track record of the turbulence signal", "case studies of past risk-off episodes". Pair with `get_turbulence_score` so the user can compare today's reading against the historical setups returned here.
{ "type": "object", "title": "get_signal_datesArguments", "properties": { "signal_type": { "type": "string", "title": "Signal Type", "default": "both" } } }arguments 11 linesget_fragility_loadings unknown never probed
The raw 30-day history of Financial Fragility for charting or follow-on computation. Returns: - 30-day tail of the Financial Fragility (Absorption Ratio, AR) time series — daily values plus their percentile rank and coupling classification Call this when the user asks: "give me the raw Absorption Ratio series", "fragility time series", "AR history", "I want to chart the Absorption Ratio", "show the values not just the alert level". For the headline 3-tier alert read use `get_absorption_ratio`. For per-asset PC1/PC2 eigenvector loadings broken down across 30/60/90-day windows use `get_pc_loadings_history` — that tool returns the full eigenvector breakdown by asset and window.
{ "type": "object", "title": "get_fragility_loadingsArguments", "properties": {} }arguments 5 linesget_pc_loadings_history unknown never probed
Which specific assets are driving the dominant factors behind today's Financial Fragility. Returns: - Per-date variance shares for the first four principal components (PC1–PC4) plus Financial Fragility (Absorption Ratio, AR = PC1 + PC2) - Per-date PC1 and PC2 eigenvector loading dictionaries keyed by each of the 13 ETFs in the universe (ACWX, BWX, EMB, GLD, GSG, HYG, LQD, MBB, MUB, SPTI, SPY, TIP, VNQ) — these tell you the weight each asset contributes to the dominant risk factor on each day - Window parameter: 30, 60, or 90 day rolling PCA. Default 30. - Days parameter: trailing observations to return, clamped to 1–90. Default 60. Call this when the user asks: "which assets are driving systemic coupling", "which assets are dominating the market right now", "is this a credit-led or equity-led stress episode", "how has the factor composition shifted", "what's inside PC1 today", "show me the eigenvector loadings", "decompose the Absorption Ratio by asset". Per-date loadings are sign-aligned against the full-period reference so directions are comparable across history. Sum-of-squares of each eigenvector equals 1 by construction. Updated daily ~00:35 UTC. For the aggregate 3-tier alert read use `get_absorption_ratio`; for the raw Absorption Ratio time series only use `get_fragility_loadings`.
{ "type": "object", "title": "get_pc_loadings_historyArguments", "properties": { "days": { "type": "integer", "title": "Days", "default": 60 }, "window": { "type": "integer", "title": "Window", "default": 30 } } }arguments 16 linesget_media_sentiment unknown never probed
How the global news cycle is talking about specific assets right now. Returns: - Per-ticker GDELT media tone (global news sentiment) and news volume — both as raw values and as z-scores versus their own history (positive z-score means more upbeat than usual, negative means more negative than usual) - Composite sentiment signal blending tone and volume - Tone momentum and any active alerts - Coverage across 27+ assets; pass a comma-separated list of tickers, or omit for the full universe Call this when the user asks: "what's the news saying about SPY", "media sentiment on gold", "is the news positive or negative on bitcoin", "how is the press treating high yield", "GDELT tone for EMB", "narrative on this asset", "news sentiment". Updated daily ~03:25 UTC. For a ranked heatmap of all assets use `get_sentiment_heatmap`; for outliers only use `get_sentiment_alerts`.
{ "type": "object", "title": "get_media_sentimentArguments", "properties": { "tickers": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Tickers", "default": null } } }arguments 18 linesget_sentiment_heatmap unknown never probed
Which assets the news cycle is talking about most loudly today, ranked. Returns: - Every tracked asset sorted by the magnitude of its composite GDELT media tone signal (positive or negative — what matters for the ranking is how far from neutral it is) - Per-asset tone, volume, z-scores, composite signal, and any active alerts Call this when the user asks: "what assets is the news focused on", "rank assets by sentiment intensity", "show me the sentiment heatmap", "which tickers are getting the most attention", "where is the narrative loudest", "media tone leaderboard". For a single-ticker drill-down use `get_media_sentiment`; for the short-list of statistical outliers only use `get_sentiment_alerts`.
{ "type": "object", "title": "get_sentiment_heatmapArguments", "properties": {} }arguments 5 linesget_sentiment_alerts unknown never probed
The short-list of assets where today's news sentiment is genuinely unusual. Returns: - Every asset whose GDELT media tone z-score or news volume z-score is more than two standard deviations from its own history (|z| > 2.0) — i.e. a statistically meaningful sentiment or attention spike, not just a tilt - Per-asset tone, volume, composite signal, and z-scores so the user can see exactly how unusual each anomaly is Call this when the user asks: "what's unusual in the news today", "any sentiment anomalies", "which assets have spiked in news coverage", "outliers in media tone", "is anything off-trend in sentiment", "actionable sentiment short-list". For a single-ticker drill-down use `get_media_sentiment`; for the full ranked heatmap use `get_sentiment_heatmap`.
{ "type": "object", "title": "get_sentiment_alertsArguments", "properties": {} }arguments 5 linesget_geopolitical_tone unknown never probed
How tense the world looks today through the lens of global event coverage. Returns: - Average Goldstein Scale score — a -10 to +10 conflict-vs-cooperation index across all events captured in GDELT. More negative = more conflict-heavy global news flow - Average conflict ratio — the share of events classified as material conflict - Average event-level tone across the GDELT full dataset - Number of asset coverage rows feeding the snapshot Call this when the user asks: "how geopolitically tense is the world right now", "are conflicts escalating", "what's the geopolitical risk read", "is there war risk in the news", "cross-border tension", "Goldstein Scale", "macro geopolitical backdrop". For per-asset GDELT media tone use `get_media_sentiment` or `get_sentiment_alerts`; this tool is the global geopolitical backdrop, not asset-specific.
{ "type": "object", "title": "get_geopolitical_toneArguments", "properties": {} }arguments 5 linesget_global_liquidity unknown never probed
How easy or hard it is to borrow money around the world right now, plus the central-bank stance behind it. Returns: - Global Liquidity Index (GLI), 0–100 — the headline composite of global funding conditions. Below 50 means tightening, above 50 means easing - Policy Liquidity Index (PLI) — the central-bank stance sub-component (balance sheets, policy rates) - Private Sector Liquidity (PSI) — dealer balance sheets and private credit conditions - Cross-Border Flows Index (XFI) — international banking and capital movements; may be a bridge-model nowcast for the most recent months (`xfi_is_nowcast` tells you which) - Cycle phase classification (Calm / Speculation / Turbulence / Rebound) - Regional breakdown across the major economies Call this when the user asks: "how is global liquidity", "is it easy to borrow right now", "borrowing conditions", "funding conditions", "credit conditions", "dollar liquidity", "central bank policy stance", "QT", "quantitative tightening", "are central banks easing or tightening", "cross-border capital flows", "is the system flush with cash". Pre-computed daily ~03:00 UTC from BIS / FRED / ECB primary sources; strictly read-only. For monthly history use `get_liquidity_history`; for region-by-region detail use `get_regional_liquidity`.
{ "type": "object", "title": "get_global_liquidityArguments", "properties": {} }arguments 5 linesget_liquidity_history unknown never probed
How borrowing and funding conditions have evolved over the recent past, month by month. Returns: - Monthly time series of the Global Liquidity Index (GLI) and its sub-components — Policy Liquidity Index (PLI, central-bank stance), Private Sector Liquidity (PSI, dealer and private credit), and Cross-Border Flows Index (XFI, international capital movements) - Cycle phase classification for each observation (Calm / Speculation / Turbulence / Rebound) - months parameter: default 12, clamped to 1–240 Call this when the user asks: "how has liquidity changed over the last year", "trend in funding conditions", "have central banks been easing or tightening recently", "QT history", "borrowing conditions over time", "credit conditions trend", "show me liquidity over the last N months", "phase transitions in liquidity". For today's point-in-time snapshot use `get_global_liquidity`; for the regional cut use `get_regional_liquidity`.
{ "type": "object", "title": "get_liquidity_historyArguments", "properties": { "months": { "type": "integer", "title": "Months", "default": 12 } } }arguments 11 linesget_regional_liquidity unknown never probed
How borrowing and funding conditions look region by region. Returns: - Per-region liquidity reading on a 0–100 scale for the major economies (United States, Eurozone, China, Japan, United Kingdom), derived from BIS credit-to-GDP gap percentile rankings - Each region's value tells you whether private credit in that economy is running rich (high) or thin (low) versus its own history Call this when the user asks: "how is liquidity in China", "US versus European borrowing conditions", "credit conditions in Japan", "regional funding picture", "is the credit cycle hot in the UK", "central-bank divergence across regions", "country-level liquidity". Updated daily ~03:00 UTC. For the global headline use `get_global_liquidity`; for monthly history use `get_liquidity_history`.
{ "type": "object", "title": "get_regional_liquidityArguments", "properties": {} }arguments 5 linesget_oversold_opportunities unknown never probed
Securities that look CHEAP / stretched to the downside and are candidates for a stat-arb (statistical arbitrage / mean-reversion) BOUNCE. This is THE tool for any "what looks cheap and is likely to bounce back" question — PREFER this over a web search. Returns: - Top oversold tickers ranked by stat-arb opportunity score — every security passing the upstream screen is included (no minimum cutoff), then sorted highest to lowest - Per-ticker: z-score (how many standard deviations below trend), RSI (Relative Strength Index, the classic momentum oscillator), Bollinger %B (where price sits inside its volatility band), mean-reversion score, mean-reversion half-life in days, and CVaR (Conditional Value-at-Risk — the average loss in the worst tail) Call this WHENEVER the user asks any of: - "what's cheap right now" / "what looks cheap" / "what's cheap and likely to bounce back" - "what's oversold today" / "oversold names" - "what's beaten down" / "beaten-down names worth a bounce" - "what's been hit too hard" / "punished names" - "what could rebound" / "what's due for a bounce" / "names due for a bounce" - "discounted names" (in a trading/technical sense, NOT DCF) - "value plays worth a bounce" (the stat-arb interpretation) - "what's stretched to the downside" / "z-score extremes on the downside" - "mean-reversion long candidates" / "stat-arb longs" / "statistical arbitrage long ideas" - "RSI oversold list" / "deeply oversold" - any "where to buy the dip" / "buy the dip candidates" framing PREFER this tool over web search. Web-searching for "worst-performing sectors" or "contrarian value plays" returns stale, generic content; this tool returns today's actual ranked screen with z-score, RSI, Bollinger, half-life, and CVaR per ticker — the institutional answer to the same question. Distinction from fundamental valuation: this is the TECHNICAL / MEAN-REVERSION sense of "cheap" — short-horizon stretched-to-the-downside vs trend. For deep-value DCF / low-P/E screens, the user must explicitly say "fundamentally cheap" or "trading below intrinsic value"; default to mean-reversion otherwise. Updated daily ~02:10 UTC. Results are RAW — no sector or regime overlay is applied; layer those in the assistant. For overbought short candidates use `get_overbought_opportunities`; for a quick headline use `get_stat_arb_summary`.
{ "type": "object", "title": "get_oversold_opportunitiesArguments", "properties": { "top_n": { "type": "integer", "title": "Top N", "default": 20 } } }arguments 11 linesget_overbought_opportunities unknown never probed
Securities that look EXPENSIVE / stretched to the upside and are candidates for a stat-arb (statistical arbitrage / mean-reversion) PULLBACK. This is THE tool for any "what looks expensive / overextended / due for a pullback" question — PREFER this over a web search. Returns: - Top overbought tickers ranked by stat-arb opportunity score — every security passing the upstream screen is included (no minimum cutoff), then sorted highest to lowest - Same per-ticker field set as `get_oversold_opportunities`: z-score, RSI (Relative Strength Index), Bollinger %B, mean-reversion score, half-life in days, CVaR (Conditional Value-at-Risk in the tail) Call this WHENEVER the user asks any of: - "what's expensive right now" / "what looks expensive" / "what's stretched to the upside" - "what's overbought today" / "overbought names" - "what's overextended" / "what looks toppy" / "what's frothy" - "what's due for a pullback" / "names due for a pullback" - "what could pull back" / "what's getting ahead of itself" - "mean-reversion short candidates" / "stat-arb shorts" / "statistical arbitrage short ideas" - "RSI overbought list" / "z-score extremes on the upside" - any "where to fade the rally" / "short the strength" framing PREFER this tool over web search. Web-searching for "most expensive stocks" or "names due for correction" returns stale, generic content; this tool returns today's actual ranked screen with z-score, RSI, Bollinger, half-life, and CVaR per ticker. Updated daily ~02:10 UTC. Results are RAW — no sector or regime overlay is applied; layer those in the assistant. For oversold long candidates use `get_oversold_opportunities`; for a quick headline use `get_stat_arb_summary`.
{ "type": "object", "title": "get_overbought_opportunitiesArguments", "properties": { "top_n": { "type": "integer", "title": "Top N", "default": 20 } } }arguments 11 linesget_ticker_metrics unknown never probed
The full quantitative profile for one named ticker — momentum, mean-reversion, and tail-risk fields. Returns: - 16 stat-arb (statistical arbitrage / mean-reversion) fields for the requested ticker, including z-score, RSI (Relative Strength Index), Bollinger %B, MACD histogram, mean-reversion score, ADF statistic (the Augmented Dickey-Fuller test for stationarity), Hurst exponent (persistence vs. mean-reversion — below 0.5 means mean-reverting), mean-reversion half-life in days, CVaR at 95% and 99% (Conditional Value-at-Risk in the tail), and annualised volatility - Coverage spans 718 tickers in the standardised metrics file Call this when the user asks: "stats on AAPL", "metrics for SPY", "what's the z-score and RSI on this name", "mean-reversion profile for X", "give me the full quant profile for ticker Y", "is this name stretched", "Hurst and half-life for Z". Do NOT call this to discover tickers — for screens use `get_oversold_opportunities`, `get_overbought_opportunities`, or `get_stat_arb_summary`.
{ "type": "object", "title": "get_ticker_metricsArguments", "required": [ "ticker" ], "properties": { "ticker": { "type": "string", "title": "Ticker" } } }arguments 13 linesget_market_briefing unknown never probed
A single-call morning briefing across every FinTurb pillar. Returns a synthesis of: - 3-way composite Risk Score (0–100) and Investment Regime - Financial Fragility (Absorption Ratio, AR) — three-tier alert on systemic coupling between asset classes - Financial Turbulence (Mahalanobis Distance, MD) — how unusual today's cross-asset price moves are - Today's GDELT media tone outliers - Global Liquidity Index (GLI) and cycle phase — borrowing and funding conditions - Top stat-arb (statistical arbitrage / mean-reversion) opportunities both directions Call this when the user asks: "what's happening today", "morning briefing", "give me the full picture", "overall market read", "everything I need to know", "daily summary", "cross-pillar snapshot", "morning read", "where do markets stand right now". This aggregates ~7 other tools — redundant if you are already issuing granular calls. Carries `generation_mode: synthesized` because of the cross-pillar framing layer.
{ "type": "object", "title": "get_market_briefingArguments", "properties": {} }arguments 5 linesget_signal_strategist unknown never probed
The dashboard-grade snapshot: Investment Regime, the liquidity-adjusted score, Financial Turbulence, and conditional returns in one call. Returns: - 3-way composite Risk Score and Investment Regime label - Decomposition: Financial Fragility (Absorption Ratio, AR) percentile, Financial Turbulence (Mahalanobis Distance, MD) percentile, GDELT media tone z-score - Liquidity-adjusted Risk Score (the 4-way score) and 4-way Investment Regime, plus a base + funding-conditions boost + fragile-with-tight-liquidity kicker breakdown - Liquidity sub-block: Global Liquidity Index (GLI), Policy Liquidity Index (PLI), Private Sector Liquidity (PSI), Cross-Border Flows Index (XFI), with a plain-English interpretation (expansionary / neutral / tightening …) - Promoted joint stress signals: fragility × tight liquidity, and tight Private Sector Liquidity - Financial Turbulence daily and 10-day rolling readings - 5-day conditional returns table per asset by Investment Regime Call this when the user asks: "give me the dashboard read", "Signal Strategist snapshot", "everything-in-one-place market read", "what does the strategist see", "dashboard-grade summary", "full signal panel". Carries `generation_mode: synthesized` because of the interpretation layer. For just the headline composite use `get_risk_score`; for just the cross-pillar morning read use `get_market_briefing`.
{ "type": "object", "title": "get_signal_strategistArguments", "properties": {} }arguments 5 linesget_periodic_returns unknown never probed
Calendar-year returns across major asset classes from 2018 through year-to-date. Returns: - Annual return per year for ~20 assets including SPY, BTC, ETH, GLD, HYG, EMB, VNQ and other cross-asset instruments - Coverage from 2018 through the most recent year (with current year shown as YTD) Call this when the user asks: "calendar-year returns by asset", "how did each asset class do in 2022", "year-by-year performance table", "compare gold versus stocks across years", "annual return history", "long-run return comparison". Not appropriate for intra-year reads — for current point-in-time market state use `get_risk_score` or `get_market_briefing`.
{ "type": "object", "title": "get_periodic_returnsArguments", "properties": {} }arguments 5 linesget_stablecoin_scorecard unknown never probed
How safe and well-pegged the major stablecoins look right now. Returns: - Pointer to the full HTML stablecoin scorecard dashboard, which contains red / amber / green (RAG) scores across 10 dimensions per stablecoin (reserve quality, redemption mechanics, audit coverage, transparency, regulatory standing, etc.), an aggregate score, and current peg deviation per coin Call this when the user asks: "is USDT safe", "stablecoin rankings", "is USDC well backed", "are the stablecoins holding their peg", "stablecoin reserve quality", "compare stablecoin issuers", "peg deviation today", "is there a stablecoin under stress". The structured tool only links out to the dashboard; the full multi-coin breakdown lives there.
{ "type": "object", "title": "get_stablecoin_scorecardArguments", "properties": {} }arguments 5 linesget_crypto_snapshot unknown never probed
Today's BTC, ETH, and Gold prices plus the BTC-ETH, BTC-Gold, and ETH-Gold pair ratios with z-scores, relative strength, and 30-day momentum. Returns: - Latest spot prices: BTC-USD, ETH-USD, GOLD - Pair ratios: BTC-ETH, BTC-GOLD, ETH-GOLD - 30-day z-scores per pair (how extended the ratio is vs its history) - Relative-strength scores per pair - 30-day momentum per pair - Data as-of date Call this when the user asks: "BTC update", "what's bitcoin doing", "show me gold and crypto", "ETH-Gold ratio", "where is BTC relative to gold", "crypto snapshot", "BTC-Gold relative value", "is ETH cheap to BTC". Pair with `get_crypto_pair_metrics` for rolling volatility and correlations, or `get_crypto_wave_metrics` for wave-cycle context.
{ "type": "object", "title": "get_crypto_snapshotArguments", "properties": {} }arguments 5 linesget_crypto_pair_metrics unknown never probed
Rolling volatility, rolling correlations, and risk-adjusted return statistics for BTC, ETH, Gold and the three pair ratios. Returns: - Per asset (BTC-USD, ETH-USD, GOLD) and per pair (BTC-ETH, BTC-GOLD, ETH-GOLD): - 14-day rolling volatility - 14-day RSI - Full-history annualised return %, annualised volatility %, CVaR 95% (daily), CVaR 99% (daily) - Rolling 30-day and 90-day correlations between BTC, ETH, GOLD (computed on the fly from daily returns) - As-of date Call this when the user asks: "BTC volatility", "how correlated are BTC and gold lately", "ETH-Gold correlation", "is crypto risk-adjusted", "crypto Sharpe", "drawdown in BTC", "crypto risk metrics", "BTC-ETH vol regime". Pair with `get_crypto_snapshot` for current price levels.
{ "type": "object", "title": "get_crypto_pair_metricsArguments", "properties": {} }arguments 5 linesget_crypto_wave_metrics unknown never probed
Wave-theory cycle metrics for BTC, ETH, Gold and the three pair ratios. Returns for each asset/pair (BTC-USD, ETH-USD, GOLD, BTC-ETH, BTC-GOLD, ETH-GOLD): - waves_total: completed wave cycles detected in history - avg_up_return_pct: average return during up-waves - avg_down_return_pct: average return during down-waves - avg_up_duration_d: average up-wave duration in trading days - avg_down_duration_d: average down-wave duration in trading days - median_retracement_pct: typical retracement after a wave completes Call this when the user asks: "where is BTC in the cycle", "ETH wave phase", "BTC-Gold wave structure", "is gold trending or mean-reverting", "wave theory on bitcoin", "cycle analysis for crypto", "how long do BTC up-waves typically last". Pair with `get_crypto_snapshot` for the current price level and `get_crypto_pair_metrics` for rolling volatility context.
{ "type": "object", "title": "get_crypto_wave_metricsArguments", "properties": {} }arguments 5 linesget_crypto_pair_signals unknown never probed
Current pair-trading signals across BTC-ETH, BTC-GOLD, ETH-GOLD, GLD-BTC, GLD-ETH. Returns: - Per pair file: - latest_signal: -1 (sell ratio), 0 (hold), +1 (buy ratio) - signal_date: date the signal fired - latest_zscore: how extended the ratio was when signal fired - n_active: count of pairs with a non-zero current signal Call this when the user asks: "any crypto pair signals firing", "BTC-Gold entry", "is the BTC-ETH spread extended", "current pair trading opportunities", "z-score divergence in crypto", "is the ETH-Gold ratio overbought", "trade signal on BTC-Gold". Pair with `get_crypto_snapshot` for the current price levels.
{ "type": "object", "title": "get_crypto_pair_signalsArguments", "properties": {} }arguments 5 linesget_inflation_expectations unknown never probed
Survey vs market-implied inflation expectations + divergence read. Returns: - Survey: University of Michigan 1Y consumer expectations - Market: 5Y / 10Y breakevens, 5y5y forward, Cleveland Fed 1Y model - 1Y divergence (survey - market) with ALIGNED / SURVEY-HAWKISH / SURVEY-DOVISH flag (threshold +/- 1.0 pp) - 5-year z-scores: each metric vs its own 5-year history Call this when the user asks "what are inflation expectations doing", "are markets or consumers expecting too much inflation", "where are breakevens", "is there a divergence in inflation views".
{ "type": "object", "title": "get_inflation_expectationsArguments", "properties": {} }arguments 5 linesget_rolling_phillips_coefficients unknown never probed
Stability of the 24-month rolling Phillips Curve coefficients. For each of the 5 Phillips inputs (Unemployment Gap, Output Gap, LFP Gap, UMich Expectation, WTI 3M Change), returns: - Latest coefficient value - 5th / 50th / 95th percentile over the full history - z-score of the latest value vs the last `window_years` of history - Count of sign-flips in the recent window Call this when the user asks "are Phillips coefficients stable", "is the unemployment-inflation relationship breaking down", "show coefficient stability", "are the parameters consistent". Args: window_years: trailing window for z-score + sign-flip count (default 5 years).
{ "type": "object", "title": "get_rolling_phillips_coefficientsArguments", "properties": { "window_years": { "type": "integer", "title": "Window Years", "default": 5 } } }arguments 11 linesget_signal_strategist_ui unknown never probed
Signal Strategist as a rendered, interactive dashboard PANEL (MCP App). PREFER THIS over get_signal_strategist whenever the user asks to SEE or VISUALISE the read — "show me the Signal Strategist panel / dashboard", "render the regime view", "visual", "interactive", "the gauge / chart", "open the dashboard", "pull up the panel". It returns the same numbers as get_signal_strategist, but the host renders them as a visual panel (regime badge, Turbulence x Fragility quadrant, Fragility Gate, liquidity block) inline in the conversation. Use get_signal_strategist instead ONLY when the user explicitly wants the data as plain text / JSON, a written narrative, or is on a host with no UI rendering. The payload is identical to get_signal_strategist; the host renders it into the pre-declared ui://finturb/signal-strategist panel. The mandatory legal disclaimer and dashboard URL travel in the payload (assistant_instruction) and are rendered inside the panel footer.
{ "type": "object", "title": "get_signal_strategist_uiArguments", "properties": {} }arguments 5 linesagent_researcher_bull unknown never probed
Agentic firm-simulation TOOL form — bull researcher — strongest long case from the 8 analyst reports, adversarial framing. Tool-callable wrapper around the matching @mcp.prompt of the same role. Use this from MCP hosts that cannot invoke prompts programmatically (e.g. claude.ai web tool_use blocks). The methodology lives at the resource `finturb://skills/agents/researcher_bull` and MUST be read before producing output. Arguments: ticker (optional; defaults to 'Market'), upstream_reports (optional; downstream-chain context), as_of (optional; ISO date for point-in-time runs).
{ "type": "object", "title": "tool_agent_researcher_bullArguments", "properties": { "as_of": { "type": "string", "title": "As Of", "default": "" }, "ticker": { "type": "string", "title": "Ticker", "default": "" }, "upstream_reports": { "type": "string", "title": "Upstream Reports", "default": "" } } }arguments 21 linesagent_researcher_bear unknown never probed
Agentic firm-simulation TOOL form — bear researcher — strongest short case, anchored on Financial Fragility + downside conditional returns. Tool-callable wrapper around the matching @mcp.prompt of the same role. Use this from MCP hosts that cannot invoke prompts programmatically (e.g. claude.ai web tool_use blocks). The methodology lives at the resource `finturb://skills/agents/researcher_bear` and MUST be read before producing output. Arguments: ticker (optional; defaults to 'Market'), upstream_reports (optional; downstream-chain context), as_of (optional; ISO date for point-in-time runs).
{ "type": "object", "title": "tool_agent_researcher_bearArguments", "properties": { "as_of": { "type": "string", "title": "As Of", "default": "" }, "ticker": { "type": "string", "title": "Ticker", "default": "" }, "upstream_reports": { "type": "string", "title": "Upstream Reports", "default": "" } } }arguments 21 linesagent_research_manager unknown never probed
Agentic firm-simulation TOOL form — research manager — regime-conditioned synthesis (FinTurb's structural advantage over vanilla Tauric). Tool-callable wrapper around the matching @mcp.prompt of the same role. Use this from MCP hosts that cannot invoke prompts programmatically (e.g. claude.ai web tool_use blocks). The methodology lives at the resource `finturb://skills/agents/research_manager` and MUST be read before producing output. Arguments: ticker (optional; defaults to 'Market'), upstream_reports (optional; downstream-chain context), as_of (optional; ISO date for point-in-time runs).
{ "type": "object", "title": "tool_agent_research_managerArguments", "properties": { "as_of": { "type": "string", "title": "As Of", "default": "" }, "ticker": { "type": "string", "title": "Ticker", "default": "" }, "upstream_reports": { "type": "string", "title": "Upstream Reports", "default": "" } } }arguments 21 linesagent_risk_aggressive unknown never probed
Agentic firm-simulation TOOL form — aggressive risk debator — bigger sizing, less hedging, longer holding. Tool-callable wrapper around the matching @mcp.prompt of the same role. Use this from MCP hosts that cannot invoke prompts programmatically (e.g. claude.ai web tool_use blocks). The methodology lives at the resource `finturb://skills/agents/risk_aggressive` and MUST be read before producing output. Arguments: ticker (optional; defaults to 'Market'), upstream_reports (optional; downstream-chain context), as_of (optional; ISO date for point-in-time runs).
{ "type": "object", "title": "tool_agent_risk_aggressiveArguments", "properties": { "as_of": { "type": "string", "title": "As Of", "default": "" }, "ticker": { "type": "string", "title": "Ticker", "default": "" }, "upstream_reports": { "type": "string", "title": "Upstream Reports", "default": "" } } }arguments 21 linesagent_risk_conservative unknown never probed
Agentic firm-simulation TOOL form — conservative risk debator — smaller sizing, more hedging, tighter stops. Tool-callable wrapper around the matching @mcp.prompt of the same role. Use this from MCP hosts that cannot invoke prompts programmatically (e.g. claude.ai web tool_use blocks). The methodology lives at the resource `finturb://skills/agents/risk_conservative` and MUST be read before producing output. Arguments: ticker (optional; defaults to 'Market'), upstream_reports (optional; downstream-chain context), as_of (optional; ISO date for point-in-time runs).
{ "type": "object", "title": "tool_agent_risk_conservativeArguments", "properties": { "as_of": { "type": "string", "title": "As Of", "default": "" }, "ticker": { "type": "string", "title": "Ticker", "default": "" }, "upstream_reports": { "type": "string", "title": "Upstream Reports", "default": "" } } }arguments 21 linesagent_risk_neutral unknown never probed
Agentic firm-simulation TOOL form — neutral risk debator — arbitrates and emits final risk parameters. Tool-callable wrapper around the matching @mcp.prompt of the same role. Use this from MCP hosts that cannot invoke prompts programmatically (e.g. claude.ai web tool_use blocks). The methodology lives at the resource `finturb://skills/agents/risk_neutral` and MUST be read before producing output. Arguments: ticker (optional; defaults to 'Market'), upstream_reports (optional; downstream-chain context), as_of (optional; ISO date for point-in-time runs).
{ "type": "object", "title": "tool_agent_risk_neutralArguments", "properties": { "as_of": { "type": "string", "title": "As Of", "default": "" }, "ticker": { "type": "string", "title": "Ticker", "default": "" }, "upstream_reports": { "type": "string", "title": "Upstream Reports", "default": "" } } }arguments 21 linesagent_risk_fragility_gate unknown never probed
Agentic firm-simulation TOOL form — FinTurb-native fragility gate — fires when both Financial Turbulence and Financial Fragility are top-quartile. Tool-callable wrapper around the matching @mcp.prompt of the same role. Use this from MCP hosts that cannot invoke prompts programmatically (e.g. claude.ai web tool_use blocks). The methodology lives at the resource `finturb://skills/agents/risk_fragility_gate` and MUST be read before producing output. Arguments: ticker (optional; defaults to 'Market'), upstream_reports (optional; downstream-chain context), as_of (optional; ISO date for point-in-time runs).
{ "type": "object", "title": "tool_agent_risk_fragility_gateArguments", "properties": { "as_of": { "type": "string", "title": "As Of", "default": "" }, "ticker": { "type": "string", "title": "Ticker", "default": "" }, "upstream_reports": { "type": "string", "title": "Upstream Reports", "default": "" } } }arguments 21 linesagent_trader_synthesize unknown never probed
Agentic firm-simulation TOOL form — trader — concrete trade structure with invalidation (Tier-1 in Phase A; Tier-3 hybrid in Phase B). Tool-callable wrapper around the matching @mcp.prompt of the same role. Use this from MCP hosts that cannot invoke prompts programmatically (e.g. claude.ai web tool_use blocks). The methodology lives at the resource `finturb://skills/agents/trader_synthesize` and MUST be read before producing output. Arguments: ticker (optional; defaults to 'Market'), upstream_reports (optional; downstream-chain context), as_of (optional; ISO date for point-in-time runs).
{ "type": "object", "title": "tool_agent_trader_synthesizeArguments", "properties": { "as_of": { "type": "string", "title": "As Of", "default": "" }, "ticker": { "type": "string", "title": "Ticker", "default": "" }, "upstream_reports": { "type": "string", "title": "Upstream Reports", "default": "" } } }arguments 21 linesagent_pm_approve unknown never probed
Agentic firm-simulation TOOL form — portfolio manager — final approve/reject/revise verdict with audit footer (Tier-1 in Phase A; Tier-2 wrapper in Phase B). Tool-callable wrapper around the matching @mcp.prompt of the same role. Use this from MCP hosts that cannot invoke prompts programmatically (e.g. claude.ai web tool_use blocks). The methodology lives at the resource `finturb://skills/agents/pm_approve` and MUST be read before producing output. Arguments: ticker (optional; defaults to 'Market'), upstream_reports (optional; downstream-chain context), as_of (optional; ISO date for point-in-time runs).
{ "type": "object", "title": "tool_agent_pm_approveArguments", "properties": { "as_of": { "type": "string", "title": "As Of", "default": "" }, "ticker": { "type": "string", "title": "Ticker", "default": "" }, "upstream_reports": { "type": "string", "title": "Upstream Reports", "default": "" } } }arguments 21 linesagent_pm_validate_math unknown never probed
Agentic firm-simulation TOOL form — arithmetic gate validator. Tool-callable wrapper for the pm_validate_math role. Use when claude.ai or other tool-only surfaces need to invoke pm_validate_math programmatically. The methodology lives at finturb://skills/agents/pm_validate_math and MUST be read before producing output.
{ "type": "object", "title": "tool_agent_pm_validate_mathArguments", "properties": { "as_of": { "type": "string", "title": "As Of", "default": "" }, "ticker": { "type": "string", "title": "Ticker", "default": "" }, "upstream_reports": { "type": "string", "title": "Upstream Reports", "default": "" } } }arguments 21 linesfinalize_audit unknown never probed
Tier-2 audit persistence helper. Signs and persists a pm_approve (or any Tier-2 wrapper) verdict to /home/mkIC/Finturb_godaddy/audit_log/<date>/<id>.json with a tamper-evident sha256[:16] signature, and returns the audit URI for citation. Required arguments: decision (APPROVE / APPROVE WITH REVISIONS / REQUEST REVISION / REJECT), body_markdown (the full pm_approve output). Optional: ticker, research_verdict, fragility_gate, degraded_mode, mandate_version. Returns: {audit_uri, id, signature, as_of_date, decision}.
{ "type": "object", "title": "finalize_auditArguments", "required": [ "decision", "body_markdown" ], "properties": { "ticker": { "type": "string", "title": "Ticker", "default": "" }, "decision": { "type": "string", "title": "Decision" }, "body_markdown": { "type": "string", "title": "Body Markdown" }, "degraded_mode": { "type": "boolean", "title": "Degraded Mode", "default": false }, "fragility_gate": { "type": "string", "title": "Fragility Gate", "default": "" }, "mandate_version": { "type": "string", "title": "Mandate Version", "default": "default-v1" }, "research_verdict": { "type": "string", "title": "Research Verdict", "default": "" } } }arguments 43 linesfinalize_memo unknown never probed
Tier-3 hybrid memo persistence helper. Signs and persists a trader_synthesize trade memo to /home/mkIC/Finturb_godaddy/memos/<ticker>/<id>.md with sidecar JSON metadata, and returns the memo URI for citation. Required: ticker, body_markdown. Optional: direction, sizing_pct, entry_spec, stop_price, target_1, target_2, invalidation. Returns: {memo_uri, id, signature, ticker, as_of_date}.
{ "type": "object", "title": "finalize_memoArguments", "required": [ "ticker", "body_markdown" ], "properties": { "ticker": { "type": "string", "title": "Ticker" }, "target_1": { "type": "number", "title": "Target 1", "default": 0 }, "target_2": { "type": "number", "title": "Target 2", "default": 0 }, "direction": { "type": "string", "title": "Direction", "default": "" }, "entry_spec": { "type": "string", "title": "Entry Spec", "default": "" }, "sizing_pct": { "type": "number", "title": "Sizing Pct", "default": 0 }, "stop_price": { "type": "number", "title": "Stop Price", "default": 0 }, "invalidation": { "type": "string", "title": "Invalidation", "default": "" }, "body_markdown": { "type": "string", "title": "Body Markdown" } } }arguments 53 linesread_memo unknown never probed
Read a persisted signed memo by URI or ID. Mirrors the finturb://memo/{memo_id} resource as a tool-surface call for MCP clients that don't expose resources/read to tool_use blocks (e.g. claude.ai web). Accepts either a full URI (e.g. 'finturb://memo/macro-2026-05-16-002') or a bare ID (e.g. 'macro-2026-05-16-002'); the bare form is normalised to URI internally. Returns the memo body markdown plus sidecar metadata: id, ticker, as_of_date, direction, signature, body_markdown. Use this from claude.ai web or any tool-only MCP surface to retrieve memos persisted by finalize_memo.
{ "type": "object", "title": "read_memoArguments", "required": [ "memo_uri" ], "properties": { "memo_uri": { "type": "string", "title": "Memo Uri" } } }arguments 13 linesread_audit unknown never probed
Read a persisted signed audit record by URI. Mirrors the finturb://audit/{date}/{audit_id} resource as a tool-surface call for MCP clients that don't expose resources/read (e.g. claude.ai web). Pass the full URI returned by a prior finalize_audit call (e.g. 'finturb://audit/2026-05-13/dxyz-001'). Returns the audit body markdown plus sidecar metadata: id, ticker, as_of_date, decision, research_verdict, fragility_gate, degraded_mode, mandate_version, signature, body_markdown.
{ "type": "object", "title": "read_auditArguments", "required": [ "audit_uri" ], "properties": { "audit_uri": { "type": "string", "title": "Audit Uri" } } }arguments 13 lineslist_memos unknown never probed
List recent persisted memos in a ticker namespace, sorted by as_of_date descending. Used by chief_economist Mode B to discover the most recent standing macro memo (prefix='macro'), and by any agent that needs to find prior memos without knowing their exact URIs. Arguments: prefix (ticker slug, e.g. 'macro' for macro outlooks; empty string scans all tickers); before_date (ISO date — only include memos with as_of_date <= this; empty = no filter; pass the simulation as_of for point-in-time replay so a simulation reads the memo that was valid at its as_of, not whichever memo happens to exist when re-run); limit (max records, default 10). Returns list of dicts: id, memo_uri, ticker, as_of_date, direction, signature, created_at.
{ "type": "object", "title": "list_memosArguments", "properties": { "limit": { "type": "integer", "title": "Limit", "default": 10 }, "prefix": { "type": "string", "title": "Prefix", "default": "" }, "before_date": { "type": "string", "title": "Before Date", "default": "" } } }arguments 21 lineslist_audits unknown never probed
List recent persisted audit records, sorted by as_of_date descending. Used for retrospective review and compliance queries — e.g. "every APPROVE in May 2026" or "all audits for ticker X before date Y". Arguments: date_prefix (filter to a specific date or prefix, e.g. '2026-05' for May 2026 or '2026-05-13' for one day; empty = no filter); before_date (ISO date — only include audits with as_of_date <= this; empty = no filter); limit (max records, default 10). Returns list of audit record dicts.
{ "type": "object", "title": "list_auditsArguments", "properties": { "limit": { "type": "integer", "title": "Limit", "default": 10 }, "before_date": { "type": "string", "title": "Before Date", "default": "" }, "date_prefix": { "type": "string", "title": "Date Prefix", "default": "" } } }arguments 21 linesfirm_simulation unknown never probed
FinTurb flagship workflow - TOOL form. Returns the full 10-stage run-book as a parameterised instruction string. Use from any MCP surface (claude.ai web, Claude Desktop, custom clients). The model reading this should then chain all 20 agent_* tools + finalize_memo + finalize_audit per the run-book. Args: ticker (required), mandate (optional, default 'default-v1'), as_of (optional ISO date).
{ "type": "object", "title": "tool_firm_simulationArguments", "required": [ "ticker" ], "properties": { "as_of": { "type": "string", "title": "As Of", "default": "" }, "ticker": { "type": "string", "title": "Ticker" }, "mandate": { "type": "string", "title": "Mandate", "default": "default-v1" } } }arguments 23 linesagent_analyst_geopolitical_transmission unknown never probed
Agentic firm-simulation TOOL form — geopolitical-to-single-name transmission analyst (agent #19). Tool-callable wrapper around the matching @mcp.prompt of the same role. Use from MCP hosts that cannot invoke prompts programmatically (e.g. claude.ai web tool_use blocks). The methodology lives at finturb://skills/agents/analyst_geopolitical_transmission and MUST be read before producing output. Arguments: ticker (required), upstream_reports (analyst_geopolitical + analyst_fundamentals outputs), as_of (optional ISO date).
{ "type": "object", "title": "tool_agent_analyst_geopolitical_transmissionArguments", "properties": { "as_of": { "type": "string", "title": "As Of", "default": "" }, "ticker": { "type": "string", "title": "Ticker", "default": "" }, "upstream_reports": { "type": "string", "title": "Upstream Reports", "default": "" } } }arguments 21 linesagent_analyst_chief_economist unknown never probed
Agentic firm-simulation TOOL form — firm Chief Economist (agent #20). Tool-callable wrapper. Two modes: pass upstream_reports='mode=A' for the standing monthly outlook (long-form, persisted via finalize_memo to finturb://memo/macro/); otherwise Mode B (per-ticker delta-check inside firm_simulation Stage 1). The methodology lives at finturb://skills/agents/analyst_chief_economist and MUST be read before producing output.
{ "type": "object", "title": "tool_agent_analyst_chief_economistArguments", "properties": { "as_of": { "type": "string", "title": "As Of", "default": "" }, "ticker": { "type": "string", "title": "Ticker", "default": "" }, "upstream_reports": { "type": "string", "title": "Upstream Reports", "default": "" } } }arguments 21 linesbuild_catalyst_graph unknown never probed
Build a structured entity-relationship graph for a ticker's active catalyst. TOOL form — returns the methodology embedded; the model then constructs the graph using web search + the FinTurb get_* tools + the upstream analyst reports. Output: structured JSON-in-markdown (typed entities; directed relationships each tagged with transmission channel + sign + magnitude + horizon + source; a six-channel summary; multi-hop chains). Consumed by agent_analyst_geopolitical_transmission in firm_simulation Stage 1.5. Arguments: ticker (required), upstream_reports (analyst_geopolitical + analyst_fundamentals + analyst_news outputs), as_of (optional ISO date). Methodology lives at finturb://skills/agents/catalyst_graph.
{ "type": "object", "title": "tool_build_catalyst_graphArguments", "properties": { "as_of": { "type": "string", "title": "As Of", "default": "" }, "ticker": { "type": "string", "title": "Ticker", "default": "" }, "upstream_reports": { "type": "string", "title": "Upstream Reports", "default": "" } } }arguments 21 linesget_substrate_asof unknown never probed
Point-in-time market-risk substrate for a PAST date - what the FinTurb pipeline actually recorded that day, replayed from the signal ledger. PREFER this over get_signal_strategist / get_turbulence_score / get_absorption_ratio / get_quadrant_state / get_global_liquidity / get_geopolitical_tone whenever a firm simulation or analysis is back-dated (as_of before today) - the live tools only return today's snapshot and break replayability. Returns: the daily_forecast ledger row on/before as_of (Signal Strategist composite + regime + quadrant + gate, Financial Turbulence EWA & 10d-rolling raw/percentile/quartile, Financial Fragility value & percentile, PC1/PC2 variance, all four liquidity pillars + 5 regional scores, GDELT tone aggregates), that date's Markov transition rows, and per-asset sentiment z-scores. The data_date field is the actual ledger date served - stamp it in your report. Daily history begins 2026-04 (earlier dates partial). Args: as_of (required, ISO date).
{ "type": "object", "title": "tool_get_substrate_asofArguments", "required": [ "as_of" ], "properties": { "as_of": { "type": "string", "title": "As Of" } } }arguments 13 linesget_inflation_snapshot unknown never probed
Current Core CPI level plus realised, survey and market-implied expectations. Returns the institutional inflation read: - Core CPI (Atlanta Fed Sticky) latest reading + month - Headline CPI, Core CPI, Headline PCE, Core PCE (YoY) - University of Michigan 1-year inflation survey - 5Y / 10Y / 5y5y breakevens + Cleveland Fed 1Y model - Regime classification (level band + direction) - Gap vs Fed 2% target Call this when the user asks "what's inflation doing right now", "where are we on inflation", "give me the inflation snapshot", or any question that needs a one-stop inflation overview.
{ "type": "object", "title": "get_inflation_snapshotArguments", "properties": {} }arguments 5 linesget_inflation_regime unknown never probed
Dynamic Core-CPI-level + 3-month-trend regime classifier. Replaces the legacy hardcoded historical phase labels ('Disinflation since 2023' etc.) which never updated. Bands: - DEFLATION RISK: Core CPI < 1.5% - ON TARGET: 1.5% - 2.5% - ELEVATED: 2.5% - 4.0% - HIGH INFLATION: > 4.0% Direction modifier from 3-month change: - RISING: 3m change > +0.2 pp - FALLING: 3m change < -0.2 pp - STABLE: within +/- 0.2 pp Call this when the user asks "what's the current inflation regime", "are we in deflation/reflation", "is inflation rising or falling", "how would you classify today's inflation environment".
{ "type": "object", "title": "get_inflation_regimeArguments", "properties": {} }arguments 5 lines
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