Haircut Sharpe ratio

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

Annotations already set readOnlyHint=false and destructiveHint=false. The description adds algorithmic transparency by listing the four correction methods and an interpretive caveat, but it does not clarify potential side effects, external dependencies, or why readOnlyHint is false for what appears to be a pure validation calculation.

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?

Two sentences with the main purpose front-loaded and no filler. The second sentence is slightly tangential but serves as a useful misuse guardrail. Overall it is compact and readable.

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?

There is no output schema, and the description never states the return shape: a single number, a set of haircut values, or a structured result per method. For a tool with six parameters and multiple statistical corrections, an agent cannot confidently predict what the tool will return or how to interpret the different haircuts.

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?

The schema already provides 100% parameter coverage with detailed descriptions. The description adds methodological linkage beyond the schema, such as the 'tests' parameter driving Bonferroni and independent-test haircuts, and 'other_sharpe_ratios_annualized' enabling Holm and BHY adjustments.

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 defines what the tool computes: the haircut Sharpe ratio needed for statistical significance after accounting for multiple testing, naming specific methods (Bonferroni, independent tests, Holm, BHY). This distinguishes it from sibling validation tools like validate_deflated_sharpe, though it lacks an explicit action verb like 'calculates' or 'returns'.

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?

The description scopes the tool to multiple-testing contexts and explicitly warns that the output is not a forecast or admission criterion, providing a when-not-to-interpret indication. However, it does not directly name alternative sibling tools for scenarios like deflated Sharpe or overfitting probability.

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