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 two genuinely useful facts beyond that: the return is a 分组结果表 and the computation is run by a real algorithm engine rather than generated by the model, which signals determinism. It does not mention reproducibility requirements (the seed) or error conditions, so it adds moderate rather than rich behavioral context.

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 short and front-loaded: a bracketed purpose tag, a trigger list, and an explicit return statement. The "（真实算法引擎执行，非大模型生成）" parenthetical is slightly promotional but does carry informational value, so there is little waste overall.

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?

With five parameters, complete schema coverage, and no output schema, the main risk is an unknown return shape — the description explicitly addresses this by stating a grouping result table is returned. What remains unaddressed is how the result is keyed or the relationship to the animal-randomization sibling, which is a minor gap for a deterministic single-purpose utility.

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%, with every parameter including seed, groups, method, block_size and n_subjects documented inline, so the schema carries the semantic load. The description's mention of 区组随机 and 分层随机 loosely corresponds to method values but adds no syntax or constraint beyond what the schema states, making the baseline 3 appropriate.

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 operation (随机化分组 / randomization allocation) and enumerates the supported variants — 随机数字表, 区组随机, 分层随机 — so an agent knows exactly what the tool produces. It stops short of differentiating itself from the closest sibling, act_animal_randomize, which also performs randomization, leaving a small ambiguity about subject type.

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 conveyed through trigger phrasing ("用户要随机分组、随机数字表、区组随机、分层随机"), which lets an agent map a user request to this tool. However, there are no exclusions and no routing guidance versus act_animal_randomize or act_design_sample_size, so the choice is only implied.

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