kruskal_wallis

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 the test is non-parametric and for multiple independent samples, but lacks details on output format (e.g., test statistic, p-value), assumptions, or limitations (e.g., handles ties). For a statistical test with no annotations, this is a significant gap in transparency.

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 concise and front-loaded, stating the tool's name, purpose, and domain in one sentence without unnecessary details. It efficiently conveys the core function, though it could be slightly more structured by explicitly separating purpose from context.

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 complexity of a statistical test, no annotations, no output schema, and low parameter coverage, the description is incomplete. It lacks details on inputs, outputs, assumptions, and comparisons to siblings. While it states the purpose, it does not provide enough context for effective use without external statistical knowledge.

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 has 0% description coverage, with parameters 'groups' and 'alpha' undocumented in the schema. The description adds no semantic information about these parameters—it does not explain what 'groups' represents (e.g., array of sample data) or 'alpha' (significance level). With low schema coverage and no compensation in the description, this is inadequate.

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 performs a Kruskal-Wallis test, which is a non-parametric ANOVA, and specifies its purpose: to test whether multiple independent samples come from the same distribution. It includes domain and category context, making the purpose explicit. However, it does not differentiate from sibling tools like 'anova_one_way' or 'mann_whitney_u', which are also statistical inference tools.

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 test's purpose but does not indicate when it is appropriate (e.g., for non-normal data or ordinal outcomes) or when other tests like one-way ANOVA or Mann-Whitney U might be better. Without such context, users must infer usage from statistical knowledge alone.

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