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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so safety is covered. The description adds the meaningful claim that results come from a real algorithm engine rather than LLM generation, which is useful trust context, but says nothing about determinism, whether the seed makes runs reproducible, or what the method parameter changes.

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: the bracketed category tag, the trigger condition, and the return value come in order with no filler. The fragmentary phrasing is slightly telegraphic but nothing is 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?

For a simple four-parameter tool with full schema coverage and no output schema, the description covers purpose and return type ('分组表'). It falls short on the statistical behavior an agent might need to explain to a user, such as reproducibility via seed and the available randomization methods.

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 (n_total, groups, method, seed) already carry descriptions and the baseline is 3. The description adds no meaning beyond that – it never mentions seeding, method choice, or how group names interact with group count.

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 concrete verb and resource: randomly assigning animals into groups, and ties it to the ARRIVE reporting requirement. An agent can tell this is animal-subject randomization rather than a generic randomizer, though the distinction from the sibling act_randomize is never made explicit.

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 implied by '用户要把动物随机分组（ARRIVE 要求）' – invoke when a user needs animal allocation for an ARRIVE-compliant study. There is no statement of when not to use it, and no routing to or from the closely-named sibling act_randomize.

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