adjust_pvalues

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

The readOnlyHint annotation already covers the safety profile. The description adds only a cryptic 'Validation: certified parity evidence' quality claim, which hints at trustworthiness but does not describe output behavior, edge cases, or handling of invalid inputs. 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?

Two short sentences with the core purpose front-loaded. The validation sentence is concise, though its jargon ('certified parity evidence') is under-specified; overall there is no padding.

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 output schema and detailed parameter schema cover return values and most invocation details. Still, with eight parameters and several method-specific siblings, the description would benefit from orienting the agent on when to call this generic tool rather than bonferroni/holm/romano_wolf, and from clarifying the expected pvalues format.

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 baseline applies. The description adds no parameter-specific meaning; the method field's schema text already enumerates accepted methods and points to romano_wolf where appropriate.

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 names a specific operation ('adjust p-values') and the context ('multiple comparisons'), so an agent can tell what the tool does. It does not, however, differentiate this generic adjustment tool from method-specific siblings like bonferroni, holm, or benjamini_hochberg.

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?

No when-to-use or when-not-to-use guidance appears in the description. It does not mention that method-specific corrections exist or that romano_wolf is the right tool for resampling-based methods; the method parameter schema supplies some of this, but the tool description itself offers no routing.

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