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

Annotations already declare readOnlyHint=true, openWorldHint=false, and destructiveHint=false, so the safety profile is covered. The description adds only 'RETURNS: 主效应与交互', which is a thin output hint with no mention of assumptions (balanced design, normality), error behavior, or result structure.

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 piped label format is scannable and front-loads the method name, but the repeated bilingual title plus an ALIASES list containing overlapping English/Chinese variants of the same term consumes space without adding selection value.

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 three-parameter statistical tool with no output schema, the definition covers what the method is and roughly what it returns. It omits design requirements and result shape, which leaves meaningful gaps for an agent that needs to prepare inputs correctly.

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 100%, so each parameter (values, group_a, group_b) is already documented in the schema, and the baseline of 3 applies. The description adds nothing about how the factor-level arrays must align with the values array, which is the main ambiguity an agent would face.

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 a specific statistical method in both English and Chinese ('Two-way ANOVA / 双因素方差分析') and names the object of analysis (two factors, main effects, interaction). That distinguishes it from one-way or post-hoc siblings such as tukey_hsd and dunn_posthoc, though it never explicitly contrasts with linear_mixed_model.

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 'USE WHEN: two factors: main effects and interaction' clause gives a usable precondition for selection. However, it offers no exclusions or alternatives — nothing tells the agent when to prefer linear_mixed_model, a post-hoc test, or a one-way ANOVA instead.

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