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

Annotations already declare readOnlyHint=true, openWorldHint=false, and destructiveHint=false, so safety is covered. The description adds genuinely useful context, namely that a real algorithm engine (not an LLM) computes the result and the return includes a 90% CI and a pass/fail equivalence judgment. It does not disclose the equivalence bounds applied (e.g. 80–125%), how non-numeric or malformed input is handled, or the minimum number of subjects, which are the real behavioral unknowns here.

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 definition is front-loaded with the tool category in brackets, then the action, then the return values — a clean structure with no filler. The phrasing is terse and slightly telegraphic ("用户要做…"), and the parenthetical about not being LLM-generated could be folded more economically, but nothing is wasteful.

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?

There is no output schema, so the description correctly fills that gap by stating the return values (90% CI and equivalence determination). Missing pieces are the equivalence acceptance criteria and the expected input format/normalization. The Cmax reference without any Cmax input parameter is a small completeness inconsistency.

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% for both parameters (受试制剂 AUC / 参比制剂 AUC), so the schema already carries parameter meaning and 3 is the baseline. The description adds no detail on ordering, pairing, units, or that the arrays must be equal-length and composed of numeric strings — and its mention of Cmax has no corresponding parameter, which could mislead.

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: a bioequivalence (BE) evaluation comparing AUC/Cmax and returning a 90% CI plus an equivalence verdict. That is far more specific than the sibling tools like act_nca_analyze or act_pkpd_eval, though it never explicitly names or rules out those siblings. The Cmax mention is slightly off since the schema only accepts AUC arrays, but the core purpose is unmistakable.

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?

"用户要做 BE 试验的 AUC/Cmax 比较" implies the usage context (BE trial comparison), which is enough to route an agent in the right general direction. However, it gives no guidance on when to prefer this over act_nca_analyze, act_pkpd_eval, or act_data_quick_stats, and no prerequisites such as minimum sample size or required data format.

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