cohens_h

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

There are no annotations provided (e.g., readOnlyHint, destructiveHint), so the description must fully disclose behavior. It only states the formulaic purpose and transformation but does not mention what the output contains (e.g., the Cohen's h value), whether it accepts invalid inputs (e.g., proportions outside 0–1), or any side effects. For a statistical effect size tool, this lacks crucial transparency about return format and error handling.

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 very short (one sentence) and front-loaded with the key purpose. It avoids unnecessary words and communicates the essential computation. While it could benefit from additional brief details on output or usage, the conciseness is appropriate for a simple tool.

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 two required parameters and an output schema present (implied from context signals), the description need not detail return values if the output schema is descriptive. However, given the lack of annotations and low schema description coverage, the description should provide more context about input constraints and interpretation. It is complete in stating the core formula but incomplete for an agent to correctly set up inputs without further assumptions.

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?

The input schema provides parameter names (p1, p2) and types (number) with no descriptions or ranges, and schema description coverage is 0%. The description adds minimal meaning by stating that p1 and p2 are proportions, but it does not clarify constraints (e.g., valid range 0–1, order sensitivity, or expected array vs. single values). Given baseline 3 for two parameters with no schema descriptions, the description adds some context but is insufficient for precise parameter usage.

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 that this tool computes Cohen's h, which is an effect size measure for the difference between two proportions using an arcsine transformation. It provides a specific verb ('Effect size for') and resource ('difference between two proportions'), which distinguishes it from other statistical tools in the sibling list that focus on tests, other effect sizes, or sample size calculations.

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 implies the tool is for comparing two proportions via the arcsine transform, which is a standard usage of Cohen's h. However, it does not specify when to choose Cohen's h over effect sizes like Cohen's d (for means) or Cramér's V (for categorical associations). No guidance is given on assumptions, such as requiring independent proportions or appropriate sample sizes, or alternative tools for related tasks.

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