{"id":220847,"recorded_at":"2026-10-26T15:02:36.884093Z","observed_at":"2026-10-26T15:02:36.884026Z","subject_kind":"endpoint","subject":"e62adeb3-892d-4492-ae39-628220a1001c","kind":"mcp.tools","method":"probe.mcp/1","body":{"count":16,"tools":[{"description":"Confront a backtest claim with its over-optimism failure modes before trusting it. Given an annualized Sharpe + the number of configurations tried + the backtest window (YYYY-MM-DD), returns: the DEFLATED Sharpe — the expected MAXIMUM Sharpe achievable by chance grows with the trial count, so a high in-sample Sharpe is a selection artifact (Bailey & López de Prado); which CRISIS REGIMES were ABSENT from the backtest window (untested, from the historical-anchor catalogue); and a base-rate caveat. If the trial count is unknown — the usual case for an agent reasoning from a backtest — the Sharpe is flagged as not-deflatable / UNPROVEN. All inputs optional; supply as many as known. Descriptive, not advisory.","inputSchema":{"properties":{"annualized_sharpe":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"description":"The claimed annualized Sharpe ratio of the backtest.","title":"Annualized Sharpe"},"asset":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"description":"Asset context for the regime-coverage check (default: SPY as the equity-crisis reference).","title":"Asset"},"backtest_end":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"description":"Backtest window end (YYYY-MM-DD).","title":"Backtest End"},"backtest_start":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"description":"Backtest window start (YYYY-MM-DD) — used to detect crisis regimes the window never contained.","title":"Backtest Start"},"frequency":{"default":252.0,"description":"Return observations per year (252 = daily bars).","title":"Frequency","type":"number"},"kurt":{"default":3.0,"description":"Kurtosis of the strategy's returns (3 = normal).","title":"Kurt","type":"number"},"n_trials":{"anyOf":[{"type":"integer"},{"type":"null"}],"default":null,"description":"Number of configurations tried before selecting this backtest — drives the deflated-Sharpe correction. Unknown → the claim is flagged UNPROVEN.","title":"N Trials"},"skew":{"default":0.0,"description":"Skewness of the strategy's returns (0 = symmetric).","title":"Skew","type":"number"}},"title":"backtest_integrityArguments","type":"object"},"name":"backtest_integrity"},{"description":"Adversarial-evaluation primitive — the semantic integration layer of the platform. Given a strategy identifier, returns a 3-layer analysis: (1) outcome metrics in the worst regimes the strategy was evaluated against, (2) vulnerability profile in the 8-dimension strategy vulnerability ontology with severity classification, (3) descriptor attribution showing which regime descriptors most strongly couple to the strategy's failure. v1 supports only 'buy_and_hold' (the outcome matrix is built once per strategy); future versions will support arbitrary strategy specs once the parser-driven strategy backtest pipeline is wired in. Read ontology://strategy-vulnerabilities for the vulnerability vocabulary.","inputSchema":{"properties":{"strategy_id":{"default":"buy_and_hold","description":"Strategy identifier; v1 supports only 'buy_and_hold'.","title":"Strategy Id","type":"string"}},"title":"challenge_strategyArguments","type":"object"},"name":"challenge_strategy"},{"description":"Single-regime introspection: returns the median behavioural descriptors of a known regime, the z-scores vs the catalogue population (so you can see what makes THIS regime distinct from the average), an English characterisation generated from the most extreme descriptors, and the top 2 nearest neighbours as a preview. Complements find_similar_regime: that tool ranks neighbours of a target, this tool tells you what a single regime IS. Read this before searching if you want to reason about one regime first.","inputSchema":{"properties":{"profile_hint":{"description":"Synthetic stress-regime identifier, e.g. 'whipsaw_synthetic_spy'. Discover valid values via the regimes://available resource.","title":"Profile Hint","type":"string"}},"required":["profile_hint"],"title":"describe_regimeArguments","type":"object"},"name":"describe_regime"},{"description":"Reveal HIDDEN risk concentration: a portfolio can be capital-diversified while its RISK is dominated by one factor. Returns the Euler risk-contribution decomposition (RC_i = w_i*(Sigma*w)_i / w'Sigma*w, summing to 1) alongside the capital weights, using the empirical covariance of real returns. For this universe each asset proxies a factor (SPY=equity-beta, TLT=duration, GOLD=real-asset, BTC=crypto). E.g. a 60/40 is ~83% equity risk; a 50/50 SPY/BTC is ~86% BTC risk despite 50/50 capital. Descriptive, not advisory.","inputSchema":{"properties":{"holdings":{"description":"Portfolio legs: list of {asset, weight} objects, e.g. [{'asset': 'SPY', 'weight': 0.6}, {'asset': 'TLT', 'weight': 0.4}]. Weights are normalised to sum to 1; assets must be in the substrate universe. Each substrate asset proxies a factor (SPY=equity beta, TLT=duration, GOLD=real asset, BTC=crypto).","items":{"additionalProperties":true,"type":"object"},"title":"Holdings","type":"array"}},"required":["holdings"],"title":"factor_decompositionArguments","type":"object"},"name":"factor_decomposition"},{"description":"Nearest-neighbour retrieval over the cached regime catalogue. Provide EITHER a reference_profile_hint (use that bundle's median descriptors as target) OR a descriptor_target dict (partial spec, missing dimensions are ignored — only the provided ones contribute to distance). Optional asset_filter restricts to one asset. Returns top_n matches with similarity_score (0..1), euclidean distance in z-score space, and per-descriptor signed deltas so the agent can see WHY a regime matched. Read ontology://regime-descriptors for the descriptor definitions, and regimes://descriptors for the full catalogue.","inputSchema":{"properties":{"asset_filter":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"description":"Restrict matches to one asset (e.g. 'SPY', 'BTC').","title":"Asset Filter"},"descriptor_target":{"anyOf":[{"additionalProperties":{"type":"number"},"type":"object"},{"type":"null"}],"default":null,"description":"Partial target spec {descriptor_name: value}; only the provided dimensions contribute to the distance. Definitions: ontology://regime-descriptors.","title":"Descriptor Target"},"reference_profile_hint":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"description":"Use this catalogue bundle's median descriptors as the search target (mutually exclusive with descriptor_target).","title":"Reference Profile Hint"},"top_n":{"default":5,"description":"Number of nearest regimes to return.","title":"Top N","type":"integer"}},"title":"find_similar_regimeArguments","type":"object"},"name":"find_similar_regime"},{"description":"Compile recorded diagnostic responses into ONE citable record — a proper process documents itself. Every envelope response (MCP and REST) is recorded automatically, keyed by its request_id. Provide explicit request_ids (compiled chronologically) or last_n for the most recent entries. Returns the entries with their gate signals (revision_required + grounding_summary each) plus a ready-to-cite markdown document; revision_required on the dossier itself flags workflows containing unaddressed gate signals. Single verbatim entries: GET /api/v1/dossier/{request_id} on the REST surface. A factual record, not an assessment — descriptive, never advisory.","inputSchema":{"properties":{"last_n":{"default":0,"description":"Alternatively: compile the N most recent recorded entries (ignored when request_ids is given).","title":"Last N","type":"integer"},"request_ids":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"description":"Explicit request_ids to compile chronologically (take them from previous responses' request_id fields).","title":"Request Ids"}},"title":"get_dossierArguments","type":"object"},"name":"get_dossier"},{"description":"Return the complete thesis for `slug`: the economic framework (pillars with [E]/[M]/[K] evidence grades, falsifiers and a deep-dive), the rule-based portfolio (asset blocks × conservative/balanced/offensive weights + sizing rationale), and the stress evidence (per-tier backtest, per-regime median drawdown, real historical episodes, pre-registered claim verdicts, and the hedge hold/break behaviour). This is the 'instant portfolio with all tested attributes'. Discover slugs with list_investment_theses(). Descriptive, not advisory — the agent decides suitability.","inputSchema":{"properties":{"slug":{"description":"Thesis slug — discover valid values via list_investment_theses().","title":"Slug","type":"string"}},"required":["slug"],"title":"get_investment_thesisArguments","type":"object"},"name":"get_investment_thesis"},{"description":"Check a portfolio against an Investment Policy Statement BEFORE accepting it — the planning step a proper process does FIRST (CFA). Provide holdings + IPS constraints (max_drawdown_tolerance as a fraction e.g. 0.15, time_horizon_years, liquidity_need 'low'|'medium'|'high'). Runs the stress test internally and flags where the proposal VIOLATES the stated policy: worst stress drawdown exceeds tolerance; a short horizon cannot absorb a deep drawdown; material holdings are less liquid than the stated need. A HARD GATE, not a score. Descriptive, not advisory.","inputSchema":{"properties":{"holdings":{"description":"Portfolio legs: list of {asset, weight} objects, e.g. [{'asset': 'SPY', 'weight': 0.6}, {'asset': 'TLT', 'weight': 0.4}]. Weights are normalised to sum to 1; assets must be in the substrate universe.","items":{"additionalProperties":true,"type":"object"},"title":"Holdings","type":"array"},"liquidity_need":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"description":"'low' | 'medium' | 'high' — violated when material holdings are less liquid than the stated need.","title":"Liquidity Need"},"max_drawdown_tolerance":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"description":"IPS drawdown tolerance as a fraction, e.g. 0.15 = a -15% maximum acceptable drawdown.","title":"Max Drawdown Tolerance"},"time_horizon_years":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"description":"Investment horizon stated in the IPS; short horizons cannot absorb deep drawdowns.","title":"Time Horizon Years"}},"required":["holdings"],"title":"ips_gateArguments","type":"object"},"name":"ips_gate"},{"description":"Discover the investment-thesis catalog. Each entry is a descriptive case study that pairs an economic framework with a rule-based portfolio and the synthetic + historical stress evidence for that allocation. Returns one compact summary per thesis (slug, title, one-liner, tags, risk tiers, framework summary, headline finding). Call get_investment_thesis(slug) for the full framework / portfolio / stress evidence, or read the thesis://{slug} resource. Descriptive, not advisory — the agent decides what is suitable.","inputSchema":{"properties":{},"title":"list_investment_thesesArguments","type":"object"},"name":"list_investment_theses"},{"description":"Distribution of multi-year wealth paths for a savings plan (monthly_contribution) or a withdrawal plan (monthly_withdrawal, inflation-indexed by default) on a portfolio from the substrate universe. Multi-year paths chain ~2y model blocks (block-bootstrap, disclosed); long-run drift is RE-ANCHORED to stated capital-market assumptions (overridable via long_run_drift; the substrate's raw stress drift would compound a structural bear universe — both are echoed in the output) while the model's path shape (vol, clustering, correlations, hedge-breaks) is kept. Costs are ON by default. Returns terminal-wealth quantiles (nominal + real), ruin/shortfall probabilities, a sequence-of-returns diagnosis (same plan, bad vs good first two years), and a drift-sensitivity block (assumptions − 2pp). Amounts in the caller's currency unit. Descriptive, not advisory — no rate, allocation, or product is recommended.","inputSchema":{"properties":{"annual_inflation":{"default":0.02,"description":"Annual inflation assumption for indexing and real-value reporting (fraction, default 0.02).","title":"Annual Inflation","type":"number"},"holdings":{"description":"Portfolio legs: list of {asset, weight} objects, e.g. [{'asset': 'SPY', 'weight': 0.6}, {'asset': 'TLT', 'weight': 0.4}]. Weights are normalised to sum to 1; assets must be in the substrate universe.","items":{"additionalProperties":true,"type":"object"},"title":"Holdings","type":"array"},"horizon_years":{"description":"Plan horizon in years (multi-year paths are chained from ~2-year model blocks).","title":"Horizon Years","type":"number"},"initial_investment":{"default":0.0,"description":"Starting capital (account currency).","title":"Initial Investment","type":"number"},"long_run_drift":{"anyOf":[{"additionalProperties":{"type":"number"},"type":"object"},{"type":"null"}],"default":null,"description":"Override the re-anchored long-run drift per asset: {ASSET: annual drift fraction}; omit for the stated capital-market assumptions.","title":"Long Run Drift"},"monthly_contribution":{"default":0.0,"description":"Fixed monthly savings contribution (savings-plan mode).","title":"Monthly Contribution","type":"number"},"monthly_withdrawal":{"default":0.0,"description":"Monthly withdrawal (withdrawal-plan mode); inflation-indexed when withdrawal_inflation_indexed is true.","title":"Monthly Withdrawal","type":"number"},"rebalance":{"default":"monthly","description":"Rebalancing frequency: 'daily' | 'monthly' | 'quarterly'.","title":"Rebalance","type":"string"},"target_amount":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"description":"Optional wealth target; the output reports the probability of reaching it.","title":"Target Amount"},"withdrawal_inflation_indexed":{"default":true,"description":"Index the monthly withdrawal to inflation.","title":"Withdrawal Inflation Indexed","type":"boolean"}},"required":["holdings","horizon_years"],"title":"long_horizon_stressArguments","type":"object"},"name":"long_horizon_stress"},{"description":"Compressed cross-category map of the current market state in ONE call: for 18 category proxies (US large-cap + tech, the 9 SPDR sectors, developed ex-US, emerging markets, long Treasuries, high-yield credit, gold, oil, Bitcoin) the operational regime (BULL/SIDEWAYS/BEAR/CRISIS), model-conditional regime probabilities over a 5- or 21-trading-day horizon, stress probability vs its unconditional baseline, a descriptive historical forward-return distribution conditional on the current regime label, and an equity-factor commonality flag (US sectors largely re-express one factor — the map is fewer independent signals than rows). Per (asset, horizon) cell only the preregistered, out-of-sample-validated model tier ships (covariate logit / persistence / unconditional — see tier_pvalues). Deliberately ships NO directional up/down forecast: regime membership is the validated signal, not return direction. Use regime_outlook for single-asset depth with as_of support. Descriptive, not a market prediction, not advisory.","inputSchema":{"properties":{"horizon_days":{"default":21,"description":"Validated horizons only: 5 or 21 trading days.","title":"Horizon Days","type":"integer"}},"title":"market_regime_mapArguments","type":"object"},"name":"market_regime_map"},{"description":"Compare two portfolios (A = reference, B = candidate revision) on IDENTICAL simulated substrate paths — a paired design, so every delta is attributable to the weights, not seed noise. Returns drawdown-distribution deltas (median/worst/quantiles), probability-weighted scenario summaries, per-scenario outcome deltas, risk-concentration shift (Euler decomposition), and which diversification failures the candidate introduces or resolves. revision_required flags a candidate that deepens the worst-path drawdown or introduces a new diversification failure — the case where a revision made robustness worse. Provide holdings_a / holdings_b as lists of {asset, weight}. Descriptive, not advisory; neither portfolio is recommended or ranked.","inputSchema":{"properties":{"holdings_a":{"description":"Reference portfolio A. Portfolio legs: list of {asset, weight} objects, e.g. [{'asset': 'SPY', 'weight': 0.6}, {'asset': 'TLT', 'weight': 0.4}]. Weights are normalised to sum to 1; assets must be in the substrate universe.","items":{"additionalProperties":true,"type":"object"},"title":"Holdings A","type":"array"},"holdings_b":{"description":"Candidate revision B, same shape — evaluated on paths identical to A's, so every delta is attributable to the weights.","items":{"additionalProperties":true,"type":"object"},"title":"Holdings B","type":"array"}},"required":["holdings_a","holdings_b"],"title":"portfolio_compareArguments","type":"object"},"name":"portfolio_compare"},{"description":"Stress a multi-asset portfolio across cross-asset regimes (baseline / risk_off_crisis / rate_shock). Provide `holdings` as a list of {asset, weight}; weights are normalised. Returns, per regime: portfolio return, worst-episode drawdown, a per-leg decomposition, and a cross_asset_finding (diversification_intact / hedge_holds / hedge_breaks / shared_drawdown) describing how the holdings behaved TOGETHER. The joint correlation structure (incl. the bond hedge that can break under rate shocks) is baked into a pre-computed substrate, so Tier-1 is instant over a fixed universe (read portfolio://universe). Optional `costs` ({rebalance: none|daily|monthly|quarterly|band, annual_costs: {asset: fraction}, transaction_cost_bps}) adds a cost_impact block: frictionless vs the stated rebalancing policy + costs via a path-loop engine with real unit accounting, paired on identical paths. The substrate is a fixed 4-asset universe (SPY, TLT, GOLD, BTC; read portfolio://universe). For ANY other ticker or a custom multi-asset book, use build_portfolio in assess mode (portfolios={name:{ticker:weight}}), which calibrates and stresses an arbitrary universe live. Descriptive, not advisory.","inputSchema":{"properties":{"costs":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"description":"Optional cost model: {'rebalance': 'monthly', 'transaction_cost_bps': float, 'annual_costs': {ASSET: annual fraction}}. Omit for the frictionless default.","title":"Costs"},"holdings":{"description":"Portfolio legs: list of {asset, weight} objects, e.g. [{'asset': 'SPY', 'weight': 0.6}, {'asset': 'TLT', 'weight': 0.4}]. Weights are normalised to sum to 1; assets must be in the substrate universe.","items":{"additionalProperties":true,"type":"object"},"title":"Holdings","type":"array"}},"required":["holdings"],"title":"portfolio_stress_testArguments","type":"object"},"name":"portfolio_stress_test"},{"description":"Model-conditional probabilities that an asset is in each market regime (BULL / SIDEWAYS / BEAR / CRISIS, operational trailing-vol/drift labels) after a 5- or 21-trading-day horizon — the probability complement to the conditional stress tools: stress tools answer 'what happens GIVEN regime X', this answers 'how likely is regime X from today's observable state'. Ships only the preregistered, out-of-sample-validated tier (covariate logit; seasonality was tested and falsified); the persistence and unconditional baselines are reported alongside so an agent can see how much the model adds. Validated assets: SPY, QQQ, GLD, TLT. Optional as_of (YYYY-MM-DD) computes the outlook at a historical date. Probabilities describe membership in operationally defined regime classes — descriptive, not a market prediction, not advisory.","inputSchema":{"properties":{"as_of":{"default":"","description":"Optional historical evaluation date (YYYY-MM-DD); empty = latest data.","title":"As Of","type":"string"},"asset":{"default":"SPY","description":"One of the out-of-sample-validated assets: 'SPY', 'QQQ', 'GLD', 'TLT'.","title":"Asset","type":"string"},"horizon_days":{"default":21,"description":"Validated horizons only: 5 or 21 trading days.","title":"Horizon Days","type":"integer"}},"title":"regime_outlookArguments","type":"object"},"name":"regime_outlook"},{"description":"Run a buy-and-hold backtest against the synthetic stress regime identified by profile_hint. Returns a structured diagnostic: robustness score (0-100), per-FM-bucket failure-behavior classification with confidence + context, and the resolved regime parameters that were actually evaluated. v1 supports only buy-and-hold. To discover available regime profile_hints, read the `regimes://available` resource. Diagnostic is descriptive, not advisory.","inputSchema":{"properties":{"profile_hint":{"description":"Synthetic stress-regime identifier, e.g. 'whipsaw_synthetic_spy'. Discover valid values via the regimes://available resource.","title":"Profile Hint","type":"string"}},"required":["profile_hint"],"title":"run_stress_testArguments","type":"object"},"name":"run_stress_test"},{"description":"Persist structured improvement feedback about a previous tool response. Provide your agent identity, the request_id you are commenting on, and one or more feedback items each carrying category (from the FeedbackCategory ontology), severity, observation, optional suggested_action, and agent_confidence (0..1). Read `feedback://insights` to see aggregated cross-agent feedback.","inputSchema":{"properties":{"agent_name":{"description":"Your agent identity (model or product name).","title":"Agent Name","type":"string"},"agent_vendor":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"description":"Vendor of the submitting agent (e.g. 'Anthropic', 'OpenAI').","title":"Agent Vendor"},"feedback_items":{"description":"One or more items, each {category (FeedbackCategory ontology), severity, observation, suggested_action?, agent_confidence (0..1)}.","items":{"additionalProperties":true,"type":"object"},"title":"Feedback Items","type":"array"},"overall_confidence":{"description":"Overall confidence in this feedback, 0..1.","title":"Overall Confidence","type":"number"},"platform_version_evaluated":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"description":"Schema/platform version the feedback refers to (e.g. 'ctys-agent-v1').","title":"Platform Version Evaluated"},"request_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"description":"request_id of the response this feedback refers to.","title":"Request Id"},"session_context":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"description":"Optional free-text context of the session/workflow the feedback arose in.","title":"Session Context"}},"required":["agent_name","feedback_items","overall_confidence"],"title":"submit_feedbackArguments","type":"object"},"name":"submit_feedback"}]},"body_sha256":"8aa8df53b68f982c0f3bbcd62253cbe545dcd1bf0af2184c04c6c887ec3187c5","prev_id":null,"leaf":"0xc91767ba9fe59c7308844757dd3f38cbd9d0cff2c6fefe81502bc1b459e1a8d4","leaf_text":"{\"body\":\"8aa8df53b68f982c0f3bbcd62253cbe545dcd1bf0af2184c04c6c887ec3187c5\",\"id\":220847,\"kind\":\"mcp.tools\",\"method\":\"probe.mcp/1\",\"observed_us\":1793026956884026,\"prev\":null,\"subject\":\"e62adeb3-892d-4492-ae39-628220a1001c\",\"subject_kind\":\"endpoint\"}","day":null,"proof":[]}