dataset_pca

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

Beyond readOnlyHint=true, the description discloses a real behavioral trait: full loadings are not returned but persisted in session.dataset.last_pca. It also bounds n_components by min(samples, features). The implicit session-state write alongside readOnlyHint is worth noting but is not a hard contradiction of the annotation.

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 with the action, then prerequisite, then per-parameter notes, then return-value caveat. Each line adds distinct information with no filler.

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 usefully explains that loadings live in session.dataset.last_pca, but it is ambiguous about what the return value itself actually contains (scores, explained variance, partial loadings). Everything needed to invoke the tool correctly is present.

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 0%, so the description carries the full burden, and it does: n_components gets a default and an upper bound (min of samples/features), and log_transform is defined as a log10 transform applied before standardization, which is semantics the schema cannot express.

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 verb and resource: run PCA on the preprocessed DatasetState. The purpose is unambiguous and the prerequisite (dataset_preprocess) anchors it in the pipeline. It does not, however, distinguish itself from similar siblings such as arf_pca_preprocessed, arf_pca_species, or plot_pca_loadings.

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?

'dataset_preprocess を先に実行しておくこと' gives an explicit precondition, which is the key usage signal for this tool. There is no statement of when not to use it or which sibling PCA tool applies to which input format, so the guidance is clear but not complete.

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