Luck-equivalent trials

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

The description discloses that the tool is calibrated by Monte Carlo and adds a caution about interpretation (not admission, not forecast). This adds value beyond the annotations (readOnlyHint=false, destructiveHint=false) by explaining the statistical nature and limitations. It does not contradict annotations, and no side effects are claimed or expected.

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

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

The description is a single, dense sentence with no fluff. It front-loads the core purpose and includes a caution. It is concise but slightly cryptic due to the technical phrasing; however, it earns its length with essential information.

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

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

The description explains the conceptual output (number of trials and probability) but is vague about the exact return format since there is no output schema. It does not specify how many values are returned or how to interpret them beyond the caution. Given the tool's complexity and lack of output schema, more detail would be needed for complete guidance.

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

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

All parameters are fully documented in the schema (100% coverage), including detailed descriptions for skew, autocorrelation, periods_per_year, etc. The description adds no additional parameter information, so the baseline of 3 applies. It does not help clarify ambiguous parameters beyond what the schema already provides.

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

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

The description clearly states the tool computes the number of skill-less strategies (equivalent trials) and the probability that the best of them would reach the observed Sharpe by luck. It distinguishes itself from siblings like validate_deflated_sharpe and validate_overfitting by focusing on luck-equivalent trials rather than deflated Sharpe or overfitting probability directly.

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

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

There is no explicit guidance on when to use this tool versus the sibling validation tools. The description warns that results are not admission or forecast, but does not state scenarios where this tool is preferred (e.g., when a Monte Carlo calibration is needed) or alternatives to avoid. The context is implied by the tool name and siblings, but not made explicit.

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