factor_analysis

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

With no annotations, the description carries the behavioral burden and does substantial work: it lists the statistical outputs and discloses that omitting n_factors uses the parallel-analysis suggestion. It does not cover data requirements, missing-data handling, or what saving a solution implies, but the disclosed behavior is already rich.

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?

Three sentences each serve a distinct purpose: naming the analysis type, listing the output bundle, and clarifying the key n_factors default behavior. No filler or redundant restatement of the schema.

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?

For a complex 10-parameter tool with no output schema, the description is quite complete: it covers the analysis family, major results, and the critical default-selection behavior. Minor gaps remain around data preparation, side effects of save_as, and the exact result object structure, but the essentials for correct invocation are 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 100%, so the baseline is 3, but the description adds value beyond parameter names by explaining that leaving n_factors empty invokes the parallel-analysis suggestion and by framing the type parameter as EFA vs PCA.

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 clearly identifies the tool as exploratory factor analysis or principal component analysis, and enumerates a specific set of outputs (KMO, Bartlett's test, loading matrix, communalities, variance, scree plot, cross-loading items). This distinguishes it from sibling statistics tools, none of which offer EFA/PCA.

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 description implies when to use the tool (dimension reduction/construct exploration) but provides no explicit when-to-use or when-not-to-use guidance and names no alternatives. The only usage directive is parameter-level advice about leaving n_factors empty, not tool selection guidance.

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