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

Annotations already mark the tool as read-only and non-destructive; the description adds meaningful behavioral context by detailing the statistical methods (Deflated Sharpe Ratio, permutation test, purged-fold decay) and stating that inputs are not retained beyond a redacted audit hash. No contradiction with annotations.

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 compact and front-loaded: purpose first, then method, then data-retention behavior, then docs link. Every sentence contributes information without redundancy.

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?

For a complex statistical tool with no output schema, the description explains the main return classification and the methodology, and the schema covers parameter details. It could be more explicit about the full response shape, but an agent has enough to select and invoke the tool correctly.

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?

With 75% schema coverage, the schema covers most parameters, and the description adds semantic context for several: 'realized returns (or trade rows)' clarifies returns/trades, 'number of variants tried' explains n_trials, and 'purged folds' and 'sign-flip permutation test' give meaning to k_folds and n_perms.

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 a specific purpose: statistically reality-checking a backtest from realized returns or trade rows, and it names the output categories (likely_real / borderline / overfit_or_noise). It does not explicitly distinguish this from sibling tools like review or verify_proof, though 'not the strategy itself' narrows the scope.

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 gives clear context for when to use the tool: when you have realized returns or trade rows from a backtest and want an overfitting reality-check. It includes an exclusion ('not the strategy itself') but does not name alternative tools or provide explicit when-not-to-use guidance.

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