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

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=false, so safety is covered. The description adds two meaningful traits beyond that: the expected return payload (PCA scores and explained variance) and the assurance that a real algorithm engine, not an LLM, computes results — relevant for trust in numbers.

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?

Front-loaded bracketed domain tag, then use case, then return contract in two short clauses. Every sentence carries information; nothing is redundant.

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?

The description covers purpose and return values even though no output schema exists, which is good. But for a nested-object input the most error-prone detail — the structure of the metabolite matrix and its alignment with groups — is left entirely unaddressed, so an agent could still format the payload incorrectly.

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 both parameters are documented at the top level (groups = group labels, data_json = metabolite matrix). However, data_json is a nested object with no declared inner properties, and the description says nothing about expected orientation, feature/sample layout, or whether groups must align by row — the schema baseline of 3 applies with no added value.

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?

States a specific method and resource (metabolomics PCA) plus the analytical intent (sample grouping trends, outlier detection), which differentiates it from siblings like act_metabo_plsda or act_metabo_heatmap by domain only. It does not explicitly name those siblings, so an agent must infer the boundary.

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?

Gives a clear trigger condition: use when the user wants to view sample grouping trends or check for outliers. There is no explicit when-not guidance and no named alternative (e.g. PLS-DA for supervised discrimination), but the use case is unambiguous.

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