rank_biserial_correlation

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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds valuable behavioral context beyond annotations: sign interpretation (positive/negative/zero), the return range [-1, 1], and rough effect-size thresholds. There is no contradiction with the 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 three sentences with no wasted words: purpose first, then usage, then interpretation. Every sentence earns its place, making it easy for an agent to parse quickly.

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?

Given the tool's simplicity, full schema coverage, clear annotations, and output schema, the description covers the essential context: what the tool computes, when to call it, what inputs to pass, and how to interpret the result. Nothing critical is missing.

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%, with all three parameters already documented clearly. The description reinforces that u1_statistic, n1, and n2 come from mann_whitney_u and the two sample sizes, but it does not add substantial new parameter-level meaning 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 opens with 'Effect size for a Mann-Whitney U test,' which precisely identifies the tool's role and distinguishes it from the many sibling statistical tests and effect sizes. It also explains the output's meaning, making the purpose unmistakable.

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 states when to use the tool: 'Call after mann_whitney_u, passing its statistic and the two sample sizes.' This gives clear contextual guidance and prerequisite sequencing, though it does not enumerate explicit exclusions or alternative tools for when not to use it.

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