one_way_anova

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

No annotations are present, so the description must cover behavior. It states the test's goal but discloses nothing about the computation, assumptions, output (e.g., F-statistic, p-value), or any side effects. This is minimal transparency for a statistical test.

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 sentence, which is concise. However, it omits critical information, making it under-specified rather than efficiently concise. For a tool this simple, slightly more detail would not harm conciseness.

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 (statistical hypothesis test with assumptions), zero annotations, no output schema, and two parameters with no descriptions, the description is severely incomplete. An agent would lack the context to invoke the tool correctly or interpret results.

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 description must explain parameters. It does not mention 'groups' (each inner array as a group of observations) or 'alpha' (significance level, default 0.05). The agent has no guidance on how to structure input or what these parameters mean.

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: testing whether three or more independent groups have different means. This distinguishes it from siblings like two_sample_t_test (two groups) and chi-square tests (categorical data).

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?

No usage guidelines are provided. The description does not mention when to use ANOVA versus alternatives (e.g., Kruskal-Wallis for non-normal data), assumptions (normality, homogeneity), or post-hoc procedures. Given the many sibling tools, this omission significantly reduces helpfulness.

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