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 meaningful context beyond that: what is returned (每组例数与合计) and that results come from a deterministic algorithm engine rather than LLM generation — a relevant trust signal for a numeric calculation.

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?

Very compact and front-loaded: purpose, trigger, and return value in three short segments. The bracketed header duplicates the annotation title, and the engine-provenance note is a slight aside, but nothing is genuinely wasted.

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?

Describing the return value compensates for the missing output schema, and the annotations cover the read-only profile. The gap is that all six parameters are optional yet sample_type determines which are actually needed — the description never explains that dependency, which is the main thing an agent could get wrong.

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 six parameters (sd, alpha, power, effect, dropout, sample_type) are already documented in the schema. The description adds no extra meaning about which parameters each sample_type requires or how defaults interact, so baseline 3 applies.

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?

States a specific verb+resource (样本量计算, 设计口径) and makes clear it answers 'how many cases per group'. The '设计口径' qualifier implicitly separates it from act_animal_sample_size and act_randomize, though it never names them.

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?

Gives a trigger condition ('用户在设计阶段要算样本量、问要做多少例'), which is real usage context. However it offers no exclusions and no explicit routing away from the animal-design or randomization siblings, leaving the agent to infer the boundary.

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