# io.github.fnobbe/crashtestyourstrategy (remote · mcp.crashtestyourstrategy.ai)

Portfolio and strategy stress diagnostics with hedge-break detection and regime outlook. Free tier.

- Trust score: 73/100 (medium)
- Change this week: +13
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-03

## Components

- remote · `mcp.crashtestyourstrategy.ai`: 73/100 (this document), [markdown](https://verifymcp.io/servers/fnobbe-crashtestyourstrategy/mcp.md), [page](https://verifymcp.io/servers/fnobbe-crashtestyourstrategy/mcp)

## Channel facts

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

## Trust breakdown

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

Scored 2026-08-03.

- **Endpoint Security**: 74/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - No authorisation is required to call this server. Every tool declares its destructiveHint and none is destructive, so open access doesn't expose one.
  - HTTPS is enforced; there's no plaintext access path.
  - HSTS check failed: the Strict-Transport-Security header is absent.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 74/100
  - 100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).
  - AI-judged instruction clarity (good).
  - Context-footprint check failed: tool/resource definitions use about 4772 tokens (~159/item across 30 items; 16 tools + 14 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 27/100
  - Stability observed for 8 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
  - Structured output schemas are declared (100% of tools); any adoption earns full credit.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### Claude

```bash
claude mcp add --transport http fnobbe-crashtestyourstrategy https://mcp.crashtestyourstrategy.ai/mcp
```

### Codex

```toml
[mcp_servers.fnobbe-crashtestyourstrategy]
url = "https://mcp.crashtestyourstrategy.ai/mcp"
```

### opencode

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

### OpenClaw

```bash
openclaw mcp add fnobbe-crashtestyourstrategy --url https://mcp.crashtestyourstrategy.ai/mcp --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  fnobbe-crashtestyourstrategy:
    url: "https://mcp.crashtestyourstrategy.ai/mcp"
```

### Other

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

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

## Changelog

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

### 2026-08-02 (score 73, +9)

- [security improvement] Authorization: unverified → partial
- [functional regression] Schema quality: 123 → 159
- [functional improvement] Tool coverage: 0% → 100%
- [cosmetic] “submit_feedback” reworded the description of “agent_vendor”
- [cosmetic] “submit_feedback” reworded the description of “feedback_items”
- [cosmetic] “submit_feedback” reworded the description of “overall_confidence”
- [cosmetic] “submit_feedback” reworded the description of “platform_version_evaluated”
- [cosmetic] “submit_feedback” reworded the description of “request_id”
- [cosmetic] “submit_feedback” reworded the description of “session_context”
- [cosmetic] “backtest_integrity” reworded the description of “annualized_sharpe”
- [cosmetic] “backtest_integrity” reworded the description of “asset”
- [cosmetic] “backtest_integrity” reworded the description of “backtest_end”
- [cosmetic] “backtest_integrity” reworded the description of “backtest_start”
- [cosmetic] “backtest_integrity” reworded the description of “frequency”
- [cosmetic] “backtest_integrity” reworded the description of “kurt”
- [cosmetic] “backtest_integrity” reworded the description of “n_trials”
- [cosmetic] “backtest_integrity” reworded the description of “skew”
- [cosmetic] “challenge_strategy” reworded the description of “strategy_id”
- [cosmetic] “describe_regime” reworded the description of “profile_hint”
- [cosmetic] “factor_decomposition” reworded the description of “holdings”
- [cosmetic] “find_similar_regime” reworded the description of “asset_filter”
- [cosmetic] “find_similar_regime” reworded the description of “descriptor_target”
- [cosmetic] “find_similar_regime” reworded the description of “reference_profile_hint”
- [cosmetic] “find_similar_regime” reworded the description of “top_n”
- [cosmetic] “get_dossier” reworded the description of “last_n”
- [cosmetic] “get_dossier” reworded the description of “request_ids”
- [cosmetic] “get_investment_thesis” reworded the description of “slug”
- [cosmetic] “ips_gate” reworded the description of “holdings”
- [cosmetic] “ips_gate” reworded the description of “liquidity_need”
- [cosmetic] “ips_gate” reworded the description of “max_drawdown_tolerance”
- [cosmetic] “ips_gate” reworded the description of “time_horizon_years”
- [cosmetic] “long_horizon_stress” reworded the description of “annual_inflation”
- [cosmetic] “long_horizon_stress” reworded the description of “holdings”
- [cosmetic] “long_horizon_stress” reworded the description of “horizon_years”
- [cosmetic] “long_horizon_stress” reworded the description of “initial_investment”
- [cosmetic] “long_horizon_stress” reworded the description of “long_run_drift”
- [cosmetic] “long_horizon_stress” reworded the description of “monthly_contribution”
- [cosmetic] “long_horizon_stress” reworded the description of “monthly_withdrawal”
- [cosmetic] “long_horizon_stress” reworded the description of “rebalance”
- [cosmetic] “long_horizon_stress” reworded the description of “target_amount”
- [cosmetic] “long_horizon_stress” reworded the description of “withdrawal_inflation_indexed”
- [cosmetic] “market_regime_map” reworded the description of “horizon_days”
- [cosmetic] “portfolio_compare” reworded the description of “holdings_a”
- [cosmetic] “portfolio_compare” reworded the description of “holdings_b”
- [cosmetic] “portfolio_stress_test” reworded the description of “costs”
- [cosmetic] “portfolio_stress_test” reworded the description of “holdings”
- [cosmetic] “regime_outlook” reworded the description of “as_of”
- [cosmetic] “regime_outlook” reworded the description of “asset”
- [cosmetic] “regime_outlook” reworded the description of “horizon_days”
- [cosmetic] “run_stress_test” reworded the description of “profile_hint”
- [cosmetic] “submit_feedback” reworded the description of “agent_name”

### 2026-08-01 (score 64, +1)

No change was recorded against any check on this day. Stability & Change Management went from 17 to 20. That category is still filling its 30-day observation window: 5 days of observed history at the previous scan, 6 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-07-31 (score 63, +1)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-30 (score 62, +1)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-28 (score 61, +1)

No change was recorded against any check on this day. Stability & Change Management went from 3 to 7. That category is still filling its 30-day observation window: 1 days of observed history at the previous scan, 2 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-07-27 (score 60, +1)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-26 (score 59)

First indexed and scored.

## MCP tools (16)

### `run_stress_test` (~134 tokens)

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.

Input parameters:

- `profile_hint` (string, required): Synthetic stress-regime identifier, e.g. 'whipsaw_synthetic_spy'. Discover valid values via the regimes://available resource.

Output parameters:

- `result` (object)

### `portfolio_stress_test` (~403 tokens)

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.

Input parameters:

- `costs`: Optional cost model: {'rebalance': 'monthly', 'transaction_cost_bps': float, 'annual_costs': {ASSET: annual fraction}}. Omit for the frictionless default.
- `holdings` (array, required): 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.

Output parameters:

- `result` (object)

### `find_similar_regime` (~222 tokens)

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 — 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.

Input parameters:

- `asset_filter`: Restrict matches to one asset (e.g. 'SPY', 'BTC').
- `descriptor_target`: Partial target spec {descriptor_name: value}; only the provided dimensions contribute to the distance. Definitions: ontology://regime-descriptors.
- `reference_profile_hint`: Use this catalogue bundle's median descriptors as the search target (mutually exclusive with descriptor_target).
- `top_n` (integer): Number of nearest regimes to return.

Output parameters:

- `result` (object)

### `describe_regime` (~144 tokens)

Describe one regime — 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.

Input parameters:

- `profile_hint` (string, required): Synthetic stress-regime identifier, e.g. 'whipsaw_synthetic_spy'. Discover valid values via the regimes://available resource.

Output parameters:

- `result` (object)

### `portfolio_compare` (~254 tokens)

Portfolio compare (paired Revise-step comparison)

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.

Input parameters:

- `holdings_a` (array, required): 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…
- `holdings_b` (array, required): Candidate revision B, same shape — evaluated on paths identical to A's, so every delta is attributable to the weights.

Output parameters:

- `result` (object)

### `get_dossier` (~182 tokens)

Get dossier (audit trail of past diagnostics)

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.

Input parameters:

- `last_n` (integer): Alternatively: compile the N most recent recorded entries (ignored when request_ids is given).
- `request_ids`: Explicit request_ids to compile chronologically (take them from previous responses' request_id fields).

Output parameters:

- `result` (object)

### `long_horizon_stress` (~491 tokens)

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 — 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.

Input parameters:

- `annual_inflation` (number): Annual inflation assumption for indexing and real-value reporting (fraction, default 0.02).
- `holdings` (array, required): 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.
- `horizon_years` (number, required): Plan horizon in years (multi-year paths are chained from ~2-year model blocks).
- `initial_investment` (number): Starting capital (account currency).
- `long_run_drift`: Override the re-anchored long-run drift per asset: {ASSET: annual drift fraction}; omit for the stated capital-market assumptions.
- `monthly_contribution` (number): Fixed monthly savings contribution (savings-plan mode).
- `monthly_withdrawal` (number): Monthly withdrawal (withdrawal-plan mode); inflation-indexed when withdrawal_inflation_indexed is true.
- `rebalance` (string): Rebalancing frequency: 'daily' | 'monthly' | 'quarterly'.
- `target_amount`: Optional wealth target; the output reports the probability of reaching it.
- `withdrawal_inflation_indexed` (boolean): Index the monthly withdrawal to inflation.

Output parameters:

- `result` (object)

### `regime_outlook` (~263 tokens)

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 — 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.

Input parameters:

- `as_of` (string): Optional historical evaluation date (YYYY-MM-DD); empty = latest data.
- `asset` (string): One of the out-of-sample-validated assets: 'SPY', 'QQQ', 'GLD', 'TLT'.
- `horizon_days` (integer): Validated horizons only: 5 or 21 trading days.

Output parameters:

- `result` (object)

### `market_regime_map` (~251 tokens)

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 — 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.

Input parameters:

- `horizon_days` (integer): Validated horizons only: 5 or 21 trading days.

Output parameters:

- `result` (object)

### `challenge_strategy` (~155 tokens)

Challenge a strategy: find what breaks it (3-layer output)

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.

Input parameters:

- `strategy_id` (string): Strategy identifier; v1 supports only 'buy_and_hold'.

Output parameters:

- `result` (object)

### `factor_decomposition` (~249 tokens)

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.

Input parameters:

- `holdings` (array, required): 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.…

Output parameters:

- `result` (object)

### `ips_gate` (~286 tokens)

IPS gate — planning-step constraint check (hard 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.

Input parameters:

- `holdings` (array, required): 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`: 'low' | 'medium' | 'high' — violated when material holdings are less liquid than the stated need.
- `max_drawdown_tolerance`: IPS drawdown tolerance as a fraction, e.g. 0.15 = a -15% maximum acceptable drawdown.
- `time_horizon_years`: Investment horizon stated in the IPS; short horizons cannot absorb deep drawdowns.

Output parameters:

- `result` (object)

### `backtest_integrity` (~346 tokens)

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 — 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.

Input parameters:

- `annualized_sharpe`: The claimed annualized Sharpe ratio of the backtest.
- `asset`: Asset context for the regime-coverage check (default: SPY as the equity-crisis reference).
- `backtest_end`: Backtest window end (YYYY-MM-DD).
- `backtest_start`: Backtest window start (YYYY-MM-DD) — used to detect crisis regimes the window never contained.
- `frequency` (number): Return observations per year (252 = daily bars).
- `kurt` (number): Kurtosis of the strategy's returns (3 = normal).
- `n_trials`: Number of configurations tried before selecting this backtest — drives the deflated-Sharpe correction. Unknown → the claim is flagged UNPROVEN.
- `skew` (number): Skewness of the strategy's returns (0 = symmetric).

Output parameters:

- `result` (object)

### `submit_feedback` (~231 tokens)

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.

Input parameters:

- `agent_name` (string, required): Your agent identity (model or product name).
- `agent_vendor`: Vendor of the submitting agent (e.g. 'Anthropic', 'OpenAI').
- `feedback_items` (array, required): One or more items, each {category (FeedbackCategory ontology), severity, observation, suggested_action?, agent_confidence (0..1)}.
- `overall_confidence` (number, required): Overall confidence in this feedback, 0..1.
- `platform_version_evaluated`: Schema/platform version the feedback refers to (e.g. 'ctys-agent-v1').
- `request_id`: request_id of the response this feedback refers to.
- `session_context`: Optional free-text context of the session/workflow the feedback arose in.

Output parameters:

- `result` (object)

### `list_investment_theses` (~116 tokens)

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 — the agent decides what is suitable.

Output parameters:

- `result` (object)

### `get_investment_thesis` (~155 tokens)

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 × 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.

Input parameters:

- `slug` (string, required): Thesis slug — discover valid values via list_investment_theses().

Output parameters:

- `result` (object)

## Diagnostics

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

## Score history

- 2026-08-03: 73
- 2026-08-02: 73
- 2026-08-01: 64
- 2026-07-31: 63
- 2026-07-30: 62
- 2026-07-29: 61
- 2026-07-28: 61
- 2026-07-27: 60
- 2026-07-26: 59

## Links

- Remote endpoint: https://mcp.crashtestyourstrategy.ai/mcp
- Repository: https://github.com/fnobbe/crashtestyourstrategy-mcp
- Website: https://crashtestyourstrategy.com/interop
- Changelog RSS feed: https://verifymcp.io/servers/fnobbe-crashtestyourstrategy/mcp/changelog.xml
- Changelog JSON feed: https://verifymcp.io/servers/fnobbe-crashtestyourstrategy/mcp/changelog.json
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