run_stress_test

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.

Parameters1

profile_hint

string

required

Raw schema

{
  "type": "object",
  "properties": {
    "profile_hint": {
      "title": "Profile Hint",
      "type": "string"
    }
  },
  "required": [
    "profile_hint"
  ],
  "title": "run_stress_testArguments"
}

portfolio_stress_test

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.

Parameters2

holdings	array	required
costs	any	optional

Raw schema

{
  "type": "object",
  "properties": {
    "holdings": {
      "items": {
        "additionalProperties": true,
        "type": "object"
      },
      "title": "Holdings",
      "type": "array"
    },
    "costs": {
      "anyOf": [
        {
          "additionalProperties": true,
          "type": "object"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Costs"
    }
  },
  "required": [
    "holdings"
  ],
  "title": "portfolio_stress_testArguments"
}

find_similar_regime

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.

Parameters4

reference_profile_hint	any	optional
descriptor_target	any	optional
asset_filter	any	optional
top_n	integer	optional

Raw schema

{
  "type": "object",
  "properties": {
    "reference_profile_hint": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Reference Profile Hint"
    },
    "descriptor_target": {
      "anyOf": [
        {
          "additionalProperties": {
            "type": "number"
          },
          "type": "object"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Descriptor Target"
    },
    "asset_filter": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Asset Filter"
    },
    "top_n": {
      "default": 5,
      "title": "Top N",
      "type": "integer"
    }
  },
  "title": "find_similar_regimeArguments"
}

describe_regime

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.

Parameters1

profile_hint

string

required

Raw schema

{
  "type": "object",
  "properties": {
    "profile_hint": {
      "title": "Profile Hint",
      "type": "string"
    }
  },
  "required": [
    "profile_hint"
  ],
  "title": "describe_regimeArguments"
}

portfolio_compare

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.

Parameters2

holdings_a	array	required
holdings_b	array	required

Raw schema

{
  "type": "object",
  "properties": {
    "holdings_a": {
      "items": {
        "additionalProperties": true,
        "type": "object"
      },
      "title": "Holdings A",
      "type": "array"
    },
    "holdings_b": {
      "items": {
        "additionalProperties": true,
        "type": "object"
      },
      "title": "Holdings B",
      "type": "array"
    }
  },
  "required": [
    "holdings_a",
    "holdings_b"
  ],
  "title": "portfolio_compareArguments"
}

get_dossier

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.

Parameters2

request_ids	any	optional
last_n	integer	optional

Raw schema

{
  "type": "object",
  "properties": {
    "request_ids": {
      "anyOf": [
        {
          "items": {
            "type": "string"
          },
          "type": "array"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Request Ids"
    },
    "last_n": {
      "default": 0,
      "title": "Last N",
      "type": "integer"
    }
  },
  "title": "get_dossierArguments"
}

long_horizon_stress

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.

Parameters10

holdings	array	required
horizon_years	number	required
initial_investment	number	optional
monthly_contribution	number	optional
monthly_withdrawal	number	optional
target_amount	any	optional
annual_inflation	number	optional
withdrawal_inflation_indexed	boolean	optional
rebalance	string	optional
long_run_drift	any	optional

Raw schema

{
  "type": "object",
  "properties": {
    "holdings": {
      "items": {
        "additionalProperties": true,
        "type": "object"
      },
      "title": "Holdings",
      "type": "array"
    },
    "horizon_years": {
      "title": "Horizon Years",
      "type": "number"
    },
    "initial_investment": {
      "default": 0,
      "title": "Initial Investment",
      "type": "number"
    },
    "monthly_contribution": {
      "default": 0,
      "title": "Monthly Contribution",
      "type": "number"
    },
    "monthly_withdrawal": {
      "default": 0,
      "title": "Monthly Withdrawal",
      "type": "number"
    },
    "target_amount": {
      "anyOf": [
        {
          "type": "number"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Target Amount"
    },
    "annual_inflation": {
      "default": 0.02,
      "title": "Annual Inflation",
      "type": "number"
    },
    "withdrawal_inflation_indexed": {
      "default": true,
      "title": "Withdrawal Inflation Indexed",
      "type": "boolean"
    },
    "rebalance": {
      "default": "monthly",
      "title": "Rebalance",
      "type": "string"
    },
    "long_run_drift": {
      "anyOf": [
        {
          "additionalProperties": {
            "type": "number"
          },
          "type": "object"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Long Run Drift"
    }
  },
  "required": [
    "holdings",
    "horizon_years"
  ],
  "title": "long_horizon_stressArguments"
}

regime_outlook

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.

Parameters3

asset	string	optional
horizon_days	integer	optional
as_of	string	optional

Raw schema

{
  "type": "object",
  "properties": {
    "asset": {
      "default": "SPY",
      "title": "Asset",
      "type": "string"
    },
    "horizon_days": {
      "default": 21,
      "title": "Horizon Days",
      "type": "integer"
    },
    "as_of": {
      "default": "",
      "title": "As Of",
      "type": "string"
    }
  },
  "title": "regime_outlookArguments"
}

challenge_strategy

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.

Parameters1

strategy_id

string

optional

Raw schema

{
  "type": "object",
  "properties": {
    "strategy_id": {
      "default": "buy_and_hold",
      "title": "Strategy Id",
      "type": "string"
    }
  },
  "title": "challenge_strategyArguments"
}

factor_decomposition

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.

Parameters1

holdings

array

required

Raw schema

{
  "type": "object",
  "properties": {
    "holdings": {
      "items": {
        "additionalProperties": true,
        "type": "object"
      },
      "title": "Holdings",
      "type": "array"
    }
  },
  "required": [
    "holdings"
  ],
  "title": "factor_decompositionArguments"
}

ips_gate

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.

Parameters4

holdings	array	required
max_drawdown_tolerance	any	optional
time_horizon_years	any	optional
liquidity_need	any	optional

Raw schema

{
  "type": "object",
  "properties": {
    "holdings": {
      "items": {
        "additionalProperties": true,
        "type": "object"
      },
      "title": "Holdings",
      "type": "array"
    },
    "max_drawdown_tolerance": {
      "anyOf": [
        {
          "type": "number"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Max Drawdown Tolerance"
    },
    "time_horizon_years": {
      "anyOf": [
        {
          "type": "number"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Time Horizon Years"
    },
    "liquidity_need": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Liquidity Need"
    }
  },
  "required": [
    "holdings"
  ],
  "title": "ips_gateArguments"
}

backtest_integrity

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.

Parameters8

annualized_sharpe	any	optional
n_trials	any	optional
backtest_start	any	optional
backtest_end	any	optional
frequency	number	optional
skew	number	optional
kurt	number	optional
asset	any	optional

Raw schema

{
  "type": "object",
  "properties": {
    "annualized_sharpe": {
      "anyOf": [
        {
          "type": "number"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Annualized Sharpe"
    },
    "n_trials": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "N Trials"
    },
    "backtest_start": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Backtest Start"
    },
    "backtest_end": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Backtest End"
    },
    "frequency": {
      "default": 252,
      "title": "Frequency",
      "type": "number"
    },
    "skew": {
      "default": 0,
      "title": "Skew",
      "type": "number"
    },
    "kurt": {
      "default": 3,
      "title": "Kurt",
      "type": "number"
    },
    "asset": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Asset"
    }
  },
  "title": "backtest_integrityArguments"
}

submit_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.

Parameters7

agent_name	string	required
feedback_items	array	required
overall_confidence	number	required
request_id	any	optional
agent_vendor	any	optional
session_context	any	optional
platform_version_evaluated	any	optional

Raw schema

{
  "type": "object",
  "properties": {
    "agent_name": {
      "title": "Agent Name",
      "type": "string"
    },
    "feedback_items": {
      "items": {
        "additionalProperties": true,
        "type": "object"
      },
      "title": "Feedback Items",
      "type": "array"
    },
    "overall_confidence": {
      "title": "Overall Confidence",
      "type": "number"
    },
    "request_id": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Request Id"
    },
    "agent_vendor": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Agent Vendor"
    },
    "session_context": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Session Context"
    },
    "platform_version_evaluated": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Platform Version Evaluated"
    }
  },
  "required": [
    "agent_name",
    "feedback_items",
    "overall_confidence"
  ],
  "title": "submit_feedbackArguments"
}

list_investment_theses

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.

Parameters

No parameters.

Raw schema

{
  "type": "object",
  "properties": {},
  "title": "list_investment_thesesArguments"
}

get_investment_thesis

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.

Parameters1

slug

string

required

Raw schema

{
  "type": "object",
  "properties": {
    "slug": {
      "title": "Slug",
      "type": "string"
    }
  },
  "required": [
    "slug"
  ],
  "title": "get_investment_thesisArguments"
}

Crashtestyourstrategy

🛠️ Key Features

🚀 Use Cases

⚡ Developer Benefits

⚠️ Limitations

run_stress_test

portfolio_stress_test

find_similar_regime

describe_regime

portfolio_compare

get_dossier

long_horizon_stress

regime_outlook

challenge_strategy

factor_decomposition

ips_gate

backtest_integrity

submit_feedback

list_investment_theses

get_investment_thesis

Install

Remote endpoint