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

Annotations supply the safety profile (destructiveHint=false, openWorldHint=true), and the description adds genuine extra context: the method is Monte Carlo and luck is defined as the best of N random tries. It also warns that a threshold crossing is neither admission nor forecast, which is useful interpretive guidance. It says nothing about why readOnlyHint is false for what reads as a pure computation, leaving an unexplained annotation/label mismatch.

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?

Three sentences, front-loaded with the core computation before the sibling pointer and the caveat. Every sentence carries content, though the opening sentence is syntactically heavy and could be tightened.

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?

With an output schema present, return values need not be explained, and annotations cover the safety profile. Purpose, sibling routing, and interpretive limits are all present; the main residual gap is the unaddressed readOnlyHint=false for a statistical computation.

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?

Schema description coverage is 100%, so the parameter meanings, defaults (autocorrelation default 0), and bounds are already documented; baseline is 3. The description only implies effective_independent_trials by calling it 'a trial count' and does not add syntax or semantics beyond the schema.

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 states a specific computation: how many skill-less strategies a search would need for its best to reach the observed Sharpe by luck, and (given a trial count) the probability of that. It names the sibling it is not (validate_deflated_sharpe), so the agent can separate the two. The phrasing is dense and inverted, which costs it a point but the substance is specific and non-tautological.

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?

It explicitly routes the agent: 'for the probability the Sharpe is real, use validate_deflated_sharpe', and the clause 'with a trial count, the chance it did' signals when to supply effective_independent_trials. The closing caveat about thresholds also frames how to read the result. There is no explicit when-not, but the alternative pointer is clear.

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