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

The annotations already declare a safe, closed-world read operation, and the description adds real value on top: it states the outputs (model and VIP) and asserts that the computation runs on a real algorithm engine rather than being LLM-generated, which is a meaningful trust signal for a statistics tool. It still does not cover preconditions such as minimum sample size or group balance.

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?

It is one compact line with the technique tag front-loaded, the use case second, and the return value last. Nothing is wasted, though the bracketed label is somewhat redundant with the annotated title.

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?

Because no output schema exists, the one line about return values is helpful, but for a multivariate modeling tool that takes a nested object as input, details such as minimum sample counts, group requirements, and the structure of the VIP output are missing.

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 coverage is 100%, so both parameters (data_json metabolomics matrix and groups label array) are already documented in the schema. The description adds no additional meaning about how the group labels must align with matrix columns, so the baseline of 3 applies.

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 technique and goal: metabolomics PLS-DA for supervised discriminant analysis and differential metabolite discovery, plus what is returned (a PLS-DA model and VIP values). This clearly distinguishes it from unsupervised siblings like act_metabo_pca, but it never names those siblings explicitly.

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?

Phrasing such as '用户要做有监督判别分析' implies the use case (supervised discrimination, as opposed to unsupervised PCA or simple volcano/heatmap visualizations), but the choice among the four sibling metabolomics tools is left to inference. There are no explicit when-not-to-use conditions or prerequisites.

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