test_anova

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

With no annotations, the description carries the full burden and discharges it well: it discloses effect sizes, Levene's test, post-hoc via estimated marginal means, the nonparametric/Welch alternatives, and the side effect that the fitted model is saved for model_diagnostics. It also specifies data-shape requirements, going well beyond the schema.

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 compact, front-loaded with the main output features, and structured as a scannable bullet list of design recipes. No sentence is filler; the final sentence about saving the model adds necessary cross-tool 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?

For a complex 13-parameter tool with no output schema or annotations, the description covers all major design families, parameter combinations, the long-format requirement, and the model_diagnostics handoff. An agent has enough to configure a correct ANOVA call and route the results appropriately.

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?

Although schema coverage is 100%, the description adds design-level semantics by showing how dv, between, within, covariates, and id combine for each ANOVA family. It also maps nonparametric and welch booleans to concrete tests, giving the agent decision rules the schema alone does not provide.

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 states the tool performs ANOVA across multiple designs ('one-way / factorial', 'ANCOVA', 'repeated measures', 'mixed'), with outputs and variants. This clearly separates it from sibling tests such as test_ttest and test_categorical, and the planned contrasts among designs make 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?

It gives explicit when-to-use guidance for each design: 'dv plus between', 'add covariates', 'within plus id', 'both between and within', plus 'nonparametric=true' for Kruskal-Wallis/Friedman and 'welch=true' when variances differ. It also warns that repeated-measures data must be in long format, which is a direct prerequisite.

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