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 genuine value by stating the computation is done by a real algorithm engine rather than model generation and by naming the return artifacts, but it says nothing about input format requirements (how times and events must be encoded) or error behavior.

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 two short clauses with the purpose front-loaded and the return value stated immediately after. The bracketed title repeats the tool's title field, which is mild redundancy, but nothing else 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?

With no output schema, the description correctly compensates by naming what is returned (KM curve and log-rank). For a four-parameter statistical tool with fully documented parameters and clear read-only annotations, the only meaningful gap is the event-encoding convention, which keeps it short of a 5.

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 of the four parameters is already labeled in Chinese (control/treatment times and events). The description adds no meaning beyond that — in particular it does not clarify the event coding (e.g., 1 = death, 0 = censored) or expected time format, even though events are declared as string arrays, which is genuinely ambiguous.

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 analysis (animal survival analysis) and the concrete outputs it produces (KM curve and log-rank test), so the agent knows exactly what the tool computes. It does not, however, differentiate itself from close siblings such as act_km_curve and act_survival_analyze, so the agent cannot tell from this text alone which survival tool to pick.

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 phrasing '用户有动物死亡时间要做生存曲线' gives an implied trigger: use this when you have animal death/follow-up data and need a survival curve. There is no explicit when-not guidance, no prerequisites (e.g., censoring conventions, minimum sample size), and no reference to the other survival-related siblings, leaving selection partly to inference.

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