statistica_factor

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

With no annotations, the description carries the full burden. It usefully discloses the default extraction method and the returned outputs (eigenvalues, loadings, communalities), but says nothing about side effects, whether results are written back to the spreadsheet, or the new-process behavior of the tool.

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 tight sentences with the core purpose front-loaded, followed by the default method and return values. No filler or redundancy.

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, but the description partially covers return values. However, for a 6-parameter analysis tool with no annotations, gaps remain around prerequisites, side effects, and how path/sheet/variables interact — more context is warranted.

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 only 50%. The description restates the method default and the factors count, both of which are already documented in the schema, and adds nothing for path, sheet, or variables. It does not fully compensate for the undocumented parameters.

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 analysis (factor analysis / principal components) and even ties it to module 2101, making it immediately distinguishable from siblings like statistica_cluster or statistica_correlation_matrix. An agent can select this tool without opening the schema.

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?

Usage is implied by the analysis name but there is no explicit when-to-use or when-not guidance relative to alternatives such as statistica_cluster or statistica_correlation_matrix. It also does not state prerequisites (e.g., data must already be loaded, numeric variables only).

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