kruskal_wallis_test

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

With no annotations provided, the description carries the burden of behavioral disclosure. It states the test is non-parametric and does not assume normality, which is relevant. However, it does not mention the return value (though an output schema exists) or any edge cases like missing data handling or the role of df_name. This is adequate but not rich.

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 well-structured: a clear definition, usage guidance, and an example in just three sentences. Every sentence adds value, and the example is practical without unnecessary detail.

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 moderate complexity and the presence of an output schema, the description is largely complete. It covers purpose, applicability, a concrete example, and distinguishes from related tests. The only minor gap is the undocumented df_name parameter, but its default value reduces the risk of confusion.

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 schema has no parameter descriptions (0% coverage), but the description clarifies that 'column' is numeric and 'group_column' represents groups. The example 'kruskal_wallis_test(column="Revenue", group_column="FlownMonth")' shows the two required parameters in context. However, the third parameter 'df_name' is not explained at all, leaving a 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 performs a Kruskal-Wallis H test, a non-parametric alternative to ANOVA, and explicitly says it compares distributions of a numeric column across 3+ groups. This is a specific verb-plus-resource statement that also distinguishes it from the sibling mann_whitney_test by noting the group count difference.

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?

It provides explicit when-to-use guidance (when normality_test indicates non-normal data) and an explicit alternative (use mann_whitney_test for 2 groups). This directly addresses usage context and differentiates from related sibling tests.

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