Canli Validation

Luck-equivalent trials

validate_luck_trials

How many skill-less strategies a search would need for its best to reach this Sharpe by luck (Monte Carlo), and with a trial count, the chance it did. States luck as the best of N random tries; for the probability the Sharpe is real, use validate_deflated_sharpe. A deflated Sharpe or overfitting probability above or below any threshold is not admission to anything and is not a forecast.

Input Schema

TableJSON Schema

Name	Required	Description
`skew`	No	Skewness; below -0.5 the reading warns the counts are too generous.
`observations`	Yes	Return observations behind the Sharpe.
`autocorrelation`	No	Lag-1 autocorrelation of returns, -1 to 1; default 0. Corrects the annualized Sharpe (Lo 2002).
`periods_per_year`	Yes	Periods per year: 252 daily, 365 crypto, 52 weekly, 12 monthly.
`observed_sharpe_annualized`	Yes	Annualized Sharpe as observed.
`effective_independent_trials`	No	Independent trials tried; adds the chance the best reached this Sharpe by luck.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`data`	No
`note`	No
`error`	No
`limits`	No
`receipt`	No
`computed`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedOct 4, 2026

removedOutput schema / additionalProperties
```
Removed value: -{}
```
removedOutput schema / properties / data / additionalProperties
```
Removed value: -{}
```

changedOutput schema / properties / error / anyOf

Previous value: -[
-  {
-    "additionalProperties": {},
-    "properties": {},
-    "type": "object"
-  },
-  {
-    "type": "null"
-  }
-]New value: +[
+  {
+    "properties": {},
+    "type": "object"
+  },
+  {
+    "type": "null"
+  }
+]

changedOutput schema / properties / receipt / anyOf

Previous value: -[
-  {
-    "additionalProperties": {},
-    "properties": {},
-    "type": "object"
-  },
-  {
-    "type": "null"
-  }
-]New value: +[
+  {
+    "properties": {},
+    "type": "object"
+  },
+  {
+    "type": "null"
+  }
+]

Changed5 schema fields changedSep 27, 2026

changedInput schema / properties / autocorrelation / description

Previous value: -"First-order autocorrelation of the returns, -1 to 1; default 0. Corrects the annualized Sharpe as Lo (2002)."New value: +"Lag-1 autocorrelation of returns, -1 to 1; default 0. Corrects the annualized Sharpe (Lo 2002)."

changedInput schema / properties / effective_independent_trials / description

Previous value: -"Independent trials tried; adds the chance that the best of them reached this Sharpe by luck."New value: +"Independent trials tried; adds the chance the best reached this Sharpe by luck."

changedInput schema / properties / observations / description

Previous value: -"Number of return observations behind the Sharpe ratio."New value: +"Return observations behind the Sharpe."

changedInput schema / properties / periods_per_year / description

Previous value: -"Observations per year: 252 daily, 365 daily crypto, 52 weekly, 12 monthly."New value: +"Periods per year: 252 daily, 365 crypto, 52 weekly, 12 monthly."

changedInput schema / properties / skew / description

Previous value: -"Skewness of the returns; below -0.5 the reading warns that the counts are too generous."New value: +"Skewness; below -0.5 the reading warns the counts are too generous."

First observedSep 27, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already provide the safety profile (readOnlyHint=false, destructiveHint=false, openWorldHint=true). The description adds Monte Carlo method context and clarifies that luck is the best of N random tries, plus an interpretation caveat. It does not explain why readOnlyHint is false, but it goes beyond the annotations in useful ways.

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?

Three front-loaded sentences: first defines the core output, second gives method and alternative, third adds a caution. No wasted words, though the final sentence is somewhat abstract and could be tightened.

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?

An output schema exists, so return values need not be explained. For a complex statistical tool, the description covers purpose, method, alternative, and interpretation caveat; required parameters are left to the schema, which is acceptable.

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

Parameters3/5

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

Schema description coverage is 100%, so all six parameters are already fully documented. The description mentions 'with a trial count' mapping to effective_independent_trials, but adds no syntax or format details beyond the schema. Baseline 3 applies when the schema does the heavy lifting.

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?

States a specific computation: how many skill-less strategies a search would need for its best to reach this Sharpe by luck, and the chance it did with a trial count. It distinguishes itself from validate_deflated_sharpe, so an agent can tell the two apart without opening schemas.

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

Usage Guidelines5/5

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

Explicitly directs to validate_deflated_sharpe for the probability the Sharpe is real, and warns that a threshold on deflated Sharpe or overfitting probability is not admission or forecast. This gives clear context for when to use this tool versus its sibling.

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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