benjamini_hochberg_correction

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

No annotations are provided, so the description carries the full burden. It discloses that the tool controls FDR (less conservative than Bonferroni) and operates on a batch of p-values. It does not mention assumptions (e.g., independence) or other behavioral traits like side effects or auth needs, which is acceptable for a statistical function.

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 extremely concise at two sentences, front-loading the main action and key differentiators immediately. No unnecessary words.

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, and the description covers purpose and comparison. However, with no output schema and missing parameter descriptions, an AI agent may need more detail on how to provide inputs (e.g., format of p-values). It is minimally adequate but not fully 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 coverage is 0% and the description does not explicitly describe the parameters. 'Batch of p-values' implies the `p_values` parameter, but `alpha` is not mentioned at all. Given the lack of schema descriptions, the description should compensate but only partially does.

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 the tool's purpose: adjusting a batch of p-values for multiple comparisons while controlling the false discovery rate. It distinguishes itself from siblings by noting it is 'less conservative than Bonferroni' and the 'standard choice when testing many hypotheses at once.'

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 explicitly compares to Bonferroni and recommends this tool for scenarios with many hypotheses. However, it does not explicitly state when not to use it (e.g., when strict family-wise error rate control is needed). The comparison provides clear context for selection.

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