anova_test

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

With no annotations, the description carries the full burden of explaining behavior. It describes the test's purpose and example usage, but does not mention important behavioral aspects such as statistical assumptions (e.g., normality, homogeneity of variances), required data types beyond the example, or whether the tool is read-only. Thus it provides some context but is missing deeper behavioral disclosure.

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 at only four sentences, each adding distinct value: definition, purpose, alternative, and example. There is no fluff or repetition, making it efficient and well-organized for an agent to quickly parse.

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 explains what the tool does, when to use it, and includes an example. However, it omits any mention of the 'df_name' parameter and does not explicitly state input preconditions (e.g., numeric column type). These gaps slightly reduce completeness, but the core functionality is well covered.

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 0% parameter description coverage, so the description must compensate. It does so partially by showing an example with 'column="Revenue"' and 'group_column="FlownMonth"', implying these are column name strings. However, the third parameter 'df_name' is not mentioned at all, and the description doesn't explicitly state the nature of the columns (numeric vs categorical). Thus it adds some meaning but leaves a gap for the unmentioned parameter.

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 is a one-way ANOVA test that compares means of a numeric column across 3+ groups, using the specific verb 'compares' and a specific resource. It also distinguishes itself from sibling tools like ttest_independent by explicitly noting the 3+ group requirement, making its purpose unambiguous.

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 explicitly states when to use the tool ('Use to determine if at least one group mean differs significantly from the others') and when not to use it, by providing the alternative 'For exactly 2 groups, ttest_independent is more appropriate.' This gives clear usage guidance and naming an alternative, which is exactly what is expected.

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