cohens_d

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool calculates but doesn't describe behavioral traits like error handling (e.g., what happens with empty arrays or invalid data), computational characteristics (e.g., performance, precision), or output format (e.g., numeric value, interpretation). For a statistical tool with zero annotation coverage, this is a significant gap.

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 a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part earns its place: the calculation action, the specific metric, the input context, and domain/category tags for additional context. It's appropriately sized for a straightforward statistical 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?

Given the tool's complexity (a statistical calculation with two required array parameters), no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It lacks details on output (what the effect size value represents, units), error conditions, and practical usage notes. The domain/category tags help, but more context is needed for effective agent use.

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 0%, so the schema provides no parameter descriptions. The description mentions 'two samples' and 'standardized difference between two means', which implicitly maps to the two array parameters (data1, data2) as the sample datasets. However, it doesn't detail parameter constraints (e.g., array lengths, numeric types) or usage semantics beyond the basic mapping. The description adds some value but doesn't fully compensate for the coverage gap.

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: 'Calculate Cohen's d effect size for two samples (standardized difference between two means)'. It specifies the verb ('Calculate'), resource ('Cohen's d effect size'), and scope ('for two samples'), making the intent unambiguous. However, it doesn't explicitly differentiate from sibling tools, though the statistical nature is implied by the domain/category tags.

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 provides no guidance on when to use this tool versus alternatives. It mentions the domain (statistics) and category (inference), which hints at context, but offers no explicit when-to-use, when-not-to-use, or named alternatives. The agent must infer usage from the tool's name and parameters alone.

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