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Check a question against the realism envelope

check_envelope

Read-onlyIdempotent

Check whether a run's horizon, statistics, presets, or scenario assumptions fall within the simulator's measured realism range before running; returns ok or refusal without running a market.

Instructions

Check whether a question falls inside the range the simulator's realism was measured for, BEFORE running it. Use it whenever a conclusion leans on a horizon longer than a year, on particular statistics, on a sector-concentrated roster, on the size of a scenario's effect or on a preset other than the default. Returns ok or a refusal that names the measurement behind it, and for a named preset that preset's measured record. Runs no market, so it answers at once.

Input Schema

Name	Required	Description
`preset`	No	The preset the run uses, by name: pt-v1, pt-v2, pt-v3, pt-v4, pt-v5, pt-v6, pt-v7, pt-v8, pt-v9, pt-v10, pt-v11, pt-v12, pt-v13, pt-v14, pt-v15, pt-v16, pt-v18, pt-v19, pt-v20, pt-v21. Omit for the shipped default, pt-v21, the only preset the envelope certifies. Any other is answered as outside, with that preset's own measured record beside the answer; the measured roster mixes are granted only on pt-v19.
`statistics`	No	Panel statistics your conclusion leans on. Known: abs_return_acf1, abs_return_acf20, abs_return_acf5, annualised_vol_pct, corr_asymmetry, corr_asymmetry_lagged, corr_persistence_acf1, cross_sectional_corr, excess_kurtosis, fear_gauge_dn1, fear_gauge_dn3, index_drift_pct, index_tail_dn3_pct, leverage_effect, return_acf1, sector_excess_corr, volume_abs_return_corr, volume_change_acf1. An unknown name is refused.
`horizon_days`	Yes	The run length you are asking about, in trading days. The certified horizon is 252.
`macro_regime`	No	true if the conclusion depends on the economy reaching a particular state, such as high inflation, stagflation or a policy crisis. run_stress_scenario sets this itself when a scenario drives inflation, growth or the cycle.
`scenario_magnitude`	No	true if the conclusion depends on the SIZE of a scenario's effect rather than its direction.
`sector_concentrated`	No	true if the roster is sector-concentrated, or the name of a measured mix: all_technology, defensive, sp500_like, tech_heavy. A mix is certified only on the preset it was measured on.

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv0.10.1Oct 7, 2026

addedInput schema / properties / preset

Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "The preset the run uses, by name: pt-v1, pt-v2, pt-v3, pt-v4, pt-v5, pt-v6, pt-v7, pt-v8, pt-v9, pt-v10, pt-v11, pt-v12, pt-v13, pt-v14, pt-v15, pt-v16, pt-v18, pt-v19, pt-v20, pt-v21. Omit for the shipped default, pt-v21, the only preset the envelope certifies. Any other is answered as outside, with that preset's own measured record beside the answer; the measured roster mixes are granted only on pt-v19.",
+  "title": "Preset"
+}

First observedv0.1.0Oct 4, 2026

TDQS

A4.3/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, non-destructive, and closed-world. Beyond that, the description discloses behavior the annotations don't: the outcome shape (ok or a refusal naming the measurement), that a named preset returns that preset's measured record, and that it 'Runs no market, so it answers at once' — a latency/no-side-effect guarantee. Useful added context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences, front-loaded with the core purpose before the usage triggers and return behavior. Dense but each sentence carries substantive information; the enumeration of trigger conditions is long but earns its space as routing guidance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Output schema exists, so return values needn't be fully specified, and the description still summarizes them (ok/refusal/measured record). Given the tool's complexity around presets and certified ranges, the description supplies enough for correct invocation, though it could say more about how partial refusals across multiple flagged dimensions combine.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema does the heavy lifting and baseline is 3. The description goes slightly further by tying the parameters to decision triggers (e.g., a horizon 'longer than a year' against the 252-day certified range, leaning on statistics or effect size), which helps an agent map its situation onto the arguments rather than just reading field types.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource ('Check whether a question falls inside the range the simulator's realism was measured for') and frames it as a pre-flight gate ('BEFORE running it'), which cleanly separates it from execution siblings like run_stress_scenario and evaluate_strategies.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives concrete trigger conditions ('horizon longer than a year', 'particular statistics', 'sector-concentrated roster', 'size of a scenario's effect', 'a preset other than the default'), which is strong when-to-use guidance. It does not name a specific alternative tool to prefer or an explicit when-not-to-use case, so it falls short of the top band.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.