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by simoncoombes
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Python
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Check a question against the realism envelope

check_envelope
Read-onlyIdempotent

Validate whether a question falls within the simulator's measured realism range before running it; returns ok or a refusal naming the limiting measurement.

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 or on the size of a scenario's effect. Returns ok or a refusal that names the measurement behind it. Runs no market, so it answers at once.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
statisticsNoPanel 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_daysYesThe run length you are asking about, in trading days. The certified horizon is 252.
macro_regimeNotrue 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_magnitudeNotrue if the conclusion depends on the SIZE of a scenario's effect rather than its direction.
sector_concentratedNotrue 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

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive/no open world, so safety is covered. The description adds non-structured facts: it returns ok or a refusal naming the measurement behind it, and that it runs no market so it answers immediately — latency and outcome shape the annotations do not convey.

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

Conciseness5/5

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

Three sentences, front-loaded with the action and the temporal constraint (BEFORE running it), then the trigger conditions, then the return/latency payoff. No filler.

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

Completeness5/5

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

With an output schema present, return values need not be spelled out, yet the description still summarizes the outcome. Purpose, timing, triggers and latency are all covered, leaving nothing an agent needs before calling.

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 baseline is 3. The description nonetheless ties parameters to decision conditions — horizon longer than a year, particular statistics, sector-concentrated roster, effect size — which adds interpretive meaning over the field-by-field schema text.

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?

Names a specific action (check) against a specific resource (the simulator's realism envelope) and states the scope: whether a question falls inside the range realism was measured for. This is clearly distinguishable from siblings like validate_strategy or describe_simulator.

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?

Gives explicit triggering conditions — horizon longer than a year, particular statistics, sector-concentrated roster, scenario effect size — and the sequencing rule (BEFORE running it). It does not name an alternative tool or state when-not to use it, so it falls short of a full routing instruction.

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

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