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 does add genuine value beyond them by noting the result comes from a real algorithmic engine rather than LLM generation, and by naming the two outputs. It omits units, the underlying volume formula, and any constraint that the days/widths/lengths arrays must be the same length.

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?

Three short clauses with the core purpose front-loaded in a bracketed label, so an agent can scan it instantly. There is mild redundancy between '要算肿瘤体积与生长曲线' and '返回：体积与生长曲线', which restates the output without adding information.

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 four optional parameters, 100% schema coverage, and no output schema, the description covers what is needed to select and call the tool: inputs are lengths/widths, outputs are volume and growth curve. The remaining gaps — array-length consistency, units, and the algorithm variant — are refinements rather than blockers.

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 each parameter (days, widths, lengths, group_name) is already documented in the schema and the baseline of 3 applies. The description corroborates the length/width inputs but adds no format, unit, or array-alignment semantics, and never mentions group_name at all.

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 verb and resource — computing tumor volume and a growth curve from length/width data — which is far more than the name alone conveys. It does not, however, distinguish itself from closely related siblings such as act_animal_tgi or act_animal_survival, which an agent could plausibly confuse with this calculation.

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?

The phrase '用户有长宽数据要算肿瘤体积与生长曲线' implies the triggering condition (user supplies length/width measurements and wants volume plus growth curve), which is useful implied guidance. There is no explicit statement of when to prefer this over act_animal_tgi or act_animal_organ_coef, and no exclusions or prerequisites.

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