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

The readOnly/non-destructive/closed-world annotations already establish that this is a safe, local computation, so the description need not cover safety. 'RETURNS: 两两比较 P 值' adds a small amount of information about the output, but it does not say whether the P values are adjusted, whether confidence intervals are included, or whether model assumptions are checked.

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 uses a piped and labeled layout (USE WHEN / ALIASES / RETURNS); it front-loads the purpose and contains no filler. The ALIASES segment is keyword-heavy but serves a genuine discovery function for Chinese and English phrasings.

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?

This is a simple, single-parameter, read-only statistical tool, and the description covers the intent and the rough return type. Still, it leaves out statistical assumptions (normality, homogeneity of variance) and the shape of the result, which matter for using the tool correctly in a pipeline.

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?

There is one parameter at 100% schema coverage, and the schema documents it as '{组名: [数值...]}' with a worked example, so the baseline is 3. The description adds nothing about constraints such as the minimum number of groups, unequal group sizes, or missing values.

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 names a specific method (Tukey HSD post-hoc) and resource (pairwise comparisons across groups), so the purpose is unambiguous. It does not separate itself from the sibling dunn_posthoc, which is the non-parametric counterpart, so an agent must know the difference between the two on its own.

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?

'USE WHEN: pairwise comparisons after one-way ANOVA with family-wise error control' states the antecedent condition for calling this tool clearly. However, it gives no exclusion rule (e.g., use dunn_posthoc for non-normal data, or two_way_anova when there are two factors), which would have routed the agent decisively.

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