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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds the useful claim that results come from a deterministic algorithm engine rather than LLM generation, which matters for trust in a statistical tool, but it says nothing about default alpha/power assumptions or method limitations.

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 compact block with the purpose front-loaded, followed by user intent and the return value. The bracketed label is somewhat redundant with the tool title, and the parenthetical engine disclaimer is promotional, though it does carry informational 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?

There is no output schema, and the description does state the returned quantity (animals per group), which is adequate for the return side. However, for a statistical tool with three optional parameters whose defaults drive results, the description leaves an agent without guidance on what happens when alpha, power or groups are omitted.

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 all four parameters (alpha, power, groups, effect_size) are already documented in structured data, setting the baseline at 3. The description adds no extra meaning such as which parameters are optional in practice or typical default values used when only effect_size is supplied.

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 resource and computation (animal experiment sample size) and states the return value ('每组动物数'), so an agent can tell it is a sample-size calculator for animal studies rather than a general one. It does not explicitly name the close sibling act_design_sample_size to disambiguate, so it stops short of a 5.

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?

Usage is only implied through trigger phrasing ('用户问动物实验要多少只、ARRIVE 要求怎么写'), which does give an agent a recognizable user-intent cue. There is no explicit when-not guidance, and the ARRIVE mention overlaps with the sibling act_animal_arrive_check without saying which to prefer for checklist-writing questions.

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