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 and the description only needs to add context. It adds 'limma-style' (implying the statistical method) and names the returned artifact, but says nothing about required input shape, minimum sample counts, multiple-testing correction, or default threshold 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 pipe-delimited tag format is front-loaded and free of filler prose, which helps scanning. However, the title is repeated verbatim as the first token, the Chinese/English title duplicates the tool name, and the seven-term alias list is bulk rather than substance.

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 bears full responsibility for describing the return value, yet it says only '差异基因清单' without indicating fields such as logFC, P-value or adjusted P-value. It also leaves unexplained why all five parameters are optional for an analysis that logically requires two expression matrices.

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 five parameters (group_a, group_b, gene_names, pval_threshold, log2fc_threshold) are already documented in structured fields; baseline 3 applies. The description adds no extra meaning such as what happens when thresholds are omitted, even though every parameter is optional.

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 title and description state a specific analysis ('Differential expression (limma-style)') and a concrete output ('RETURNS: 差异基因清单'), so an agent knows this is a DEG computation over an expression matrix rather than a generic stats tool. It does not differentiate itself from the closely related sibling rnaseq_deg, which an agent would also plausibly pick for the same task.

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?

'USE WHEN: differential genes from an expression matrix (with volcano plot input)' gives a usable trigger condition, including the downstream volcano-plot use case. There is no when-not guidance and no explicit routing against alternatives such as rnaseq_deg or metabolite_volcano, so the agent must infer which DEG tool to call.

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