Serveur MCP
crashtestyourstrategy
io.github.fnobbe/crashtestyourstrategy
Ce que fait ce MCP
Provides portfolio stress testing, regime analysis, factor risk decomposition, backtest integrity checks, and descriptive investment thesis diagnostics.
Capacités
Outils
16
outils
backtest_integrity
Backtest integrity check (deflated Sharpe + regime coverage)
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 â 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 â the usual case for an agent reasoning from a backtest â the Sharpe is flagged as not-deflatable / UNPROVEN. All inputs optional; supply as many as known. Descriptive, not advisory.
Lecture seule
Idempotent
Schéma d’entrée
{'type': 'object', 'title': 'backtest_integrityArguments', 'properties': {'kurt': {'type': 'number', 'title': 'Kurt', 'default': 3.0, 'description': "Kurtosis of the strategy's returns (3 = normal)."}, 'skew': {'type': 'number', 'title': 'Skew', 'default': 0.0, 'description': "Skewness of the strategy's returns (0 = symmetric)."}, 'asset': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Asset', 'default': None, 'description': 'Asset context for the regime-coverage check (default: SPY as the equity-crisis reference).'}, 'n_trials': {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'title': 'N Trials', 'default': None, 'description': 'Number of configurations tried before selecting this backtest â\x80\x94 drives the deflated-Sharpe correction. Unknown â\x86\x92 the claim is flagged UNPROVEN.'}, 'frequency': {'type': 'number', 'title': 'Frequency', 'default': 252.0, 'description': 'Return observations per year (252 = daily bars).'}, 'backtest_end': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Backtest End', 'default': None, 'description': 'Backtest window end (YYYY-MM-DD).'}, 'backtest_start': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Backtest Start', 'default': None, 'description': 'Backtest window start (YYYY-MM-DD) â\x80\x94 used to detect crisis regimes the window never contained.'}, 'annualized_sharpe': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Annualized Sharpe', 'default': None, 'description': 'The claimed annualized Sharpe ratio of the backtest.'}}}
Schéma de sortie
{'type': 'object', 'title': 'backtest_integrityOutput', 'required': ['result'], 'properties': {'result': {'type': 'object', 'title': 'Result', 'additionalProperties': True}}}
challenge_strategy
Challenge a strategy: find what breaks it (3-layer output)
Adversarial-evaluation primitive â 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.
Lecture seule
Idempotent
Schéma d’entrée
{'type': 'object', 'title': 'challenge_strategyArguments', 'properties': {'strategy_id': {'type': 'string', 'title': 'Strategy Id', 'default': 'buy_and_hold', 'description': "Strategy identifier; v1 supports only 'buy_and_hold'."}}}
Schéma de sortie
{'type': 'object', 'title': 'challenge_strategyOutput', 'required': ['result'], 'properties': {'result': {'type': 'object', 'title': 'Result', 'additionalProperties': True}}}
describe_regime
Describe one regime â self-portrait
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.
Lecture seule
Idempotent
Schéma d’entrée
{'type': 'object', 'title': 'describe_regimeArguments', 'required': ['profile_hint'], 'properties': {'profile_hint': {'type': 'string', 'title': 'Profile Hint', 'description': "Synthetic stress-regime identifier, e.g. 'whipsaw_synthetic_spy'. Discover valid values via the regimes://available resource."}}}
Schéma de sortie
{'type': 'object', 'title': 'describe_regimeOutput', 'required': ['result'], 'properties': {'result': {'type': 'object', 'title': 'Result', 'additionalProperties': True}}}
factor_decomposition
Factor / concentration decomposition (capital weight vs risk)
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.
Lecture seule
Idempotent
Schéma d’entrée
{'type': 'object', 'title': 'factor_decompositionArguments', 'required': ['holdings'], 'properties': {'holdings': {'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}, 'title': '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)."}}}
Schéma de sortie
{'type': 'object', 'title': 'factor_decompositionOutput', 'required': ['result'], 'properties': {'result': {'type': 'object', 'title': 'Result', 'additionalProperties': True}}}
find_similar_regime
Find similar regime via behavioural descriptors
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 â 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.
Lecture seule
Idempotent
Schéma d’entrée
{'type': 'object', 'title': 'find_similar_regimeArguments', 'properties': {'top_n': {'type': 'integer', 'title': 'Top N', 'default': 5, 'description': 'Number of nearest regimes to return.'}, 'asset_filter': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Asset Filter', 'default': None, 'description': "Restrict matches to one asset (e.g. 'SPY', 'BTC')."}, 'descriptor_target': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'number'}}, {'type': 'null'}], 'title': 'Descriptor Target', 'default': None, 'description': 'Partial target spec {descriptor_name: value}; only the provided dimensions contribute to the distance. Definitions: ontology://regime-descriptors.'}, 'reference_profile_hint': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Reference Profile Hint', 'default': None, 'description': "Use this catalogue bundle's median descriptors as the search target (mutually exclusive with descriptor_target)."}}}
Schéma de sortie
{'type': 'object', 'title': 'find_similar_regimeOutput', 'required': ['result'], 'properties': {'result': {'type': 'object', 'title': 'Result', 'additionalProperties': True}}}
get_dossier
Get dossier (audit trail of past diagnostics)
Compile recorded diagnostic responses into ONE citable record â 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 â descriptive, never advisory.
Lecture seule
Idempotent
Schéma d’entrée
{'type': 'object', 'title': 'get_dossierArguments', 'properties': {'last_n': {'type': 'integer', 'title': 'Last N', 'default': 0, 'description': 'Alternatively: compile the N most recent recorded entries (ignored when request_ids is given).'}, 'request_ids': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Request Ids', 'default': None, 'description': "Explicit request_ids to compile chronologically (take them from previous responses' request_id fields)."}}}
Schéma de sortie
{'type': 'object', 'title': 'get_dossierOutput', 'required': ['result'], 'properties': {'result': {'type': 'object', 'title': 'Result', 'additionalProperties': True}}}
get_investment_thesis
Get one investment thesis (full case study)
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 à 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 â the agent decides suitability.
Lecture seule
Idempotent
Schéma d’entrée
{'type': 'object', 'title': 'get_investment_thesisArguments', 'required': ['slug'], 'properties': {'slug': {'type': 'string', 'title': 'Slug', 'description': 'Thesis slug â\x80\x94 discover valid values via list_investment_theses().'}}}
Schéma de sortie
{'type': 'object', 'title': 'get_investment_thesisOutput', 'required': ['result'], 'properties': {'result': {'type': 'object', 'title': 'Result', 'additionalProperties': True}}}
ips_gate
IPS gate â planning-step constraint check (hard gate)
Check a portfolio against an Investment Policy Statement BEFORE accepting it â 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.
Lecture seule
Idempotent
Schéma d’entrée
{'type': 'object', 'title': 'ips_gateArguments', 'required': ['holdings'], 'properties': {'holdings': {'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}, 'title': '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."}, 'liquidity_need': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Liquidity Need', 'default': None, 'description': "'low' | 'medium' | 'high' â\x80\x94 violated when material holdings are less liquid than the stated need."}, 'time_horizon_years': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Time Horizon Years', 'default': None, 'description': 'Investment horizon stated in the IPS; short horizons cannot absorb deep drawdowns.'}, 'max_drawdown_tolerance': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Max Drawdown Tolerance', 'default': None, 'description': 'IPS drawdown tolerance as a fraction, e.g. 0.15 = a -15% maximum acceptable drawdown.'}}}
Schéma de sortie
{'type': 'object', 'title': 'ips_gateOutput', 'required': ['result'], 'properties': {'result': {'type': 'object', 'title': 'Result', 'additionalProperties': True}}}
list_investment_theses
List investment theses (catalog discovery)
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 â the agent decides what is suitable.
Lecture seule
Idempotent
Schéma d’entrée
{'type': 'object', 'title': 'list_investment_thesesArguments', 'properties': {}}
Schéma de sortie
{'type': 'object', 'title': 'list_investment_thesesOutput', 'required': ['result'], 'properties': {'result': {'type': 'object', 'title': 'Result', 'additionalProperties': True}}}
long_horizon_stress
Long-horizon wealth-path stress (savings / withdrawal plans)
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 â 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 â 2pp). Amounts in the caller's currency unit. Descriptive, not advisory â no rate, allocation, or product is recommended.
Lecture seule
Idempotent
Schéma d’entrée
{'type': 'object', 'title': 'long_horizon_stressArguments', 'required': ['holdings', 'horizon_years'], 'properties': {'holdings': {'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}, 'title': '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."}, 'rebalance': {'type': 'string', 'title': 'Rebalance', 'default': 'monthly', 'description': "Rebalancing frequency: 'daily' | 'monthly' | 'quarterly'."}, 'horizon_years': {'type': 'number', 'title': 'Horizon Years', 'description': 'Plan horizon in years (multi-year paths are chained from ~2-year model blocks).'}, 'target_amount': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Target Amount', 'default': None, 'description': 'Optional wealth target; the output reports the probability of reaching it.'}, 'long_run_drift': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'number'}}, {'type': 'null'}], 'title': 'Long Run Drift', 'default': None, 'description': 'Override the re-anchored long-run drift per asset: {ASSET: annual drift fraction}; omit for the stated capital-market assumptions.'}, 'annual_inflation': {'type': 'number', 'title': 'Annual Inflation', 'default': 0.02, 'description': 'Annual inflation assumption for indexing and real-value reporting (fraction, default 0.02).'}, 'initial_investment': {'type': 'number', 'title': 'Initial Investment', 'default': 0.0, 'description': 'Starting capital (account currency).'}, 'monthly_withdrawal': {'type': 'number', 'title': 'Monthly Withdrawal', 'default': 0.0, 'description': 'Monthly withdrawal (withdrawal-plan mode); inflation-indexed when withdrawal_inflation_indexed is true.'}, 'monthly_contribution': {'type': 'number', 'title': 'Monthly Contribution', 'default': 0.0, 'description': 'Fixed monthly savings contribution (savings-plan mode).'}, 'withdrawal_inflation_indexed': {'type': 'boolean', 'title': 'Withdrawal Inflation Indexed', 'default': True, 'description': 'Index the monthly withdrawal to inflation.'}}}
Schéma de sortie
{'type': 'object', 'title': 'long_horizon_stressOutput', 'required': ['result'], 'properties': {'result': {'type': 'object', 'title': 'Result', 'additionalProperties': True}}}
market_regime_map
Market regime map (18 category proxies, h=5/21)
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 â 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 â 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.
Lecture seule
Idempotent
Schéma d’entrée
{'type': 'object', 'title': 'market_regime_mapArguments', 'properties': {'horizon_days': {'type': 'integer', 'title': 'Horizon Days', 'default': 21, 'description': 'Validated horizons only: 5 or 21 trading days.'}}}
Schéma de sortie
{'type': 'object', 'title': 'market_regime_mapOutput', 'required': ['result'], 'properties': {'result': {'type': 'object', 'title': 'Result', 'additionalProperties': True}}}
portfolio_compare
Portfolio compare (paired Revise-step comparison)
Compare two portfolios (A = reference, B = candidate revision) on IDENTICAL simulated substrate paths â 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 â 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.
Lecture seule
Idempotent
Schéma d’entrée
{'type': 'object', 'title': 'portfolio_compareArguments', 'required': ['holdings_a', 'holdings_b'], 'properties': {'holdings_a': {'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}, 'title': '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."}, 'holdings_b': {'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}, 'title': 'Holdings B', 'description': "Candidate revision B, same shape â\x80\x94 evaluated on paths identical to A's, so every delta is attributable to the weights."}}}
Schéma de sortie
{'type': 'object', 'title': 'portfolio_compareOutput', 'required': ['result'], 'properties': {'result': {'type': 'object', 'title': 'Result', 'additionalProperties': True}}}
portfolio_stress_test
Portfolio stress (multi-asset, Tier-1)
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.
Lecture seule
Idempotent
Schéma d’entrée
{'type': 'object', 'title': 'portfolio_stress_testArguments', 'required': ['holdings'], 'properties': {'costs': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Costs', 'default': None, 'description': "Optional cost model: {'rebalance': 'monthly', 'transaction_cost_bps': float, 'annual_costs': {ASSET: annual fraction}}. Omit for the frictionless default."}, 'holdings': {'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}, 'title': '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."}}}
Schéma de sortie
{'type': 'object', 'title': 'portfolio_stress_testOutput', 'required': ['result'], 'properties': {'result': {'type': 'object', 'title': 'Result', 'additionalProperties': True}}}
regime_outlook
Regime-probability outlook (validated assets, h=5/21)
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 â 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 â descriptive, not a market prediction, not advisory.
Lecture seule
Idempotent
Schéma d’entrée
{'type': 'object', 'title': 'regime_outlookArguments', 'properties': {'as_of': {'type': 'string', 'title': 'As Of', 'default': '', 'description': 'Optional historical evaluation date (YYYY-MM-DD); empty = latest data.'}, 'asset': {'type': 'string', 'title': 'Asset', 'default': 'SPY', 'description': "One of the out-of-sample-validated assets: 'SPY', 'QQQ', 'GLD', 'TLT'."}, 'horizon_days': {'type': 'integer', 'title': 'Horizon Days', 'default': 21, 'description': 'Validated horizons only: 5 or 21 trading days.'}}}
Schéma de sortie
{'type': 'object', 'title': 'regime_outlookOutput', 'required': ['result'], 'properties': {'result': {'type': 'object', 'title': 'Result', 'additionalProperties': True}}}
run_stress_test
Run stress test (buy-and-hold, v1)
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.
Lecture seule
Idempotent
Schéma d’entrée
{'type': 'object', 'title': 'run_stress_testArguments', 'required': ['profile_hint'], 'properties': {'profile_hint': {'type': 'string', 'title': 'Profile Hint', 'description': "Synthetic stress-regime identifier, e.g. 'whipsaw_synthetic_spy'. Discover valid values via the regimes://available resource."}}}
Schéma de sortie
{'type': 'object', 'title': 'run_stress_testOutput', 'required': ['result'], 'properties': {'result': {'type': 'object', 'title': 'Result', 'additionalProperties': True}}}
submit_feedback
Submit structured feedback
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.
Schéma d’entrée
{'type': 'object', 'title': 'submit_feedbackArguments', 'required': ['agent_name', 'feedback_items', 'overall_confidence'], 'properties': {'agent_name': {'type': 'string', 'title': 'Agent Name', 'description': 'Your agent identity (model or product name).'}, 'request_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Request Id', 'default': None, 'description': 'request_id of the response this feedback refers to.'}, 'agent_vendor': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Agent Vendor', 'default': None, 'description': "Vendor of the submitting agent (e.g. 'Anthropic', 'OpenAI')."}, 'feedback_items': {'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}, 'title': 'Feedback Items', 'description': 'One or more items, each {category (FeedbackCategory ontology), severity, observation, suggested_action?, agent_confidence (0..1)}.'}, 'session_context': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Session Context', 'default': None, 'description': 'Optional free-text context of the session/workflow the feedback arose in.'}, 'overall_confidence': {'type': 'number', 'title': 'Overall Confidence', 'description': 'Overall confidence in this feedback, 0..1.'}, 'platform_version_evaluated': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Platform Version Evaluated', 'default': None, 'description': "Schema/platform version the feedback refers to (e.g. 'ctys-agent-v1')."}}}
Schéma de sortie
{'type': 'object', 'title': 'submit_feedbackOutput', 'required': ['result'], 'properties': {'result': {'type': 'object', 'title': 'Result', 'additionalProperties': True}}}
Historique
Modifications récentes des outils
Ajouté
get_investment_thesis
17 September 2026 12:41
Ajouté
list_investment_theses
17 September 2026 12:41
Ajouté
submit_feedback
17 September 2026 12:41
Ajouté
backtest_integrity
17 September 2026 12:41
Ajouté
ips_gate
17 September 2026 12:41
Ajouté
factor_decomposition
17 September 2026 12:41
Ajouté
challenge_strategy
17 September 2026 12:41
Ajouté
market_regime_map
17 September 2026 12:41
Ajouté
regime_outlook
17 September 2026 12:41
Ajouté
long_horizon_stress
17 September 2026 12:41
Ajouté
get_dossier
17 September 2026 12:41
Ajouté
portfolio_compare
17 September 2026 12:41
Ajouté
describe_regime
17 September 2026 12:41
Ajouté
find_similar_regime
17 September 2026 12:41
Ajouté
portfolio_stress_test
17 September 2026 12:41
Ajouté
run_stress_test
17 September 2026 12:41
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io.github.Capital-W-Holdings/us-property-parcel-real-estate-debt
Provides US commercial real estate, parcel, debt maturity, bank CRE exposure, private capital, investor, ownership, occupancy, an…
simplefunctions
io.github.spfunctions/simplefunctions
Provides prediction-market data, thesis analysis, market signals, portfolio tracking, and automated intent-based trading workflow…
EventTrader MCP
com.cymetica/event-trader-mcp
Provides prediction-market research and trading capabilities, including real-time event-market data, CLOB execution paths, fund i…
Signal8
ai.signal8/mcp
Provides SEC filings, dilution data, insider and institutional ownership, and political-trade data.