bonferroni

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

The readOnlyHint annotation already establishes the safety profile, and the formula adds a precise behavioral description of the correction. The sentence 'Validation: certified parity evidence' gestures at validation behavior but is cryptic, so it only partially discloses what the agent can expect.

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 core formula is front-loaded in a very short description, which is efficient. However, the second sentence, 'Validation: certified parity evidence,' is opaque and does not clearly earn its place, so the description is not quite top-tier in structure.

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 tool is simple, has an output schema, and carries a read-only annotation, so the description need not exhaustively document return values. Still, it omits when-to-use guidance and leaves S and the validation claim unexplained, making it adequate but not complete.

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 of 3 applies even though the description adds little parameter-level detail. The formula introduces S without defining it or explaining how it is derived from pvalues or data_path, so it cannot be credited with stronger semantic guidance.

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 precise operation, Bonferroni correction, and supplies the formula p_adj = min(p * S, 1), so an agent can tell exactly what transformation is applied. It does not explicitly contrast itself with sibling p-value adjustment tools like adjust_pvalues, holm, or benjamini_hochberg, so it misses the top score.

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 guidance about when to use this tool versus sibling multiple-testing adjustments, nor any exclusion like 'use Benjamini-Hochberg when controlling FDR.' The intended context is only implied by the tool's name and formula, not stated.

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