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

Annotations already declare readOnlyHint, openWorldHint=false and destructiveHint=false, so the safety profile is covered. The description adds a genuine behavioral detail the annotations cannot convey: calibration is against the mean control dCt, and the returned stat_method may be welch_ttest_on_dct or student_ttest_on_dct. However, that RETURNS block is truncated mid-token, and nothing is said about minimum replicate count or what happens with unequal group sizes.

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 lead segment repeats the title verbatim in both Chinese and English, which is pure redundancy with the title field, and the ALIASES list of eleven terms consumes a large share of a short description. The remaining USE WHEN / RETURNS structure is well front-loaded, but the trailing content is cut off, leaving the structure visibly incomplete.

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 is the only place return values can be explained, and it does list fc, fold_values and p_value, which is the right instinct. But the listing is truncated and missing key operational facts for a statistics tool: required minimum n per group, whether Ct inputs are technical replicate means, and the default significance test selection rule.

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% and every parameter already carries a Chinese description with examples and pairing constraints (exp_ref_ct 与 exp_ct 一一对应). The description adds only the interpretive note that ctrl_ct/ctrl_ref_ct set the calibration baseline, which is marginal added meaning beyond the schema.

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 USE WHEN clause names a specific verb chain and resource: 'compute dCt, ddCt and 2^-ddCt fold change from target and reference Ct values, with a group test'. That is precise enough to distinguish it from the nearest sibling, qpcr_bayesian_design, which is a design rather than a calculation tool.

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 'USE WHEN' clause gives a concrete triggering condition (target and reference Ct values available, group comparison with control-calibrated dCt), and the alias list helps with fuzzy matching from user phrasing. It stops short of naming alternatives or stating when not to use it.

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