statistica_regression

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

No annotations are provided, so the description carries the full burden. It names the underlying STATISTICA module but says nothing about whether a new process is spawned, what permissions/data state it requires, or what the result looks like; the critical live-edit caveat lives only in the `attach` schema description.

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?

Brief, but the rhetorical 'The first predictor list entry is the dependent variable? No:' reads as a confused self-correction rather than front-loaded guidance, costing clarity for an agent scanning the text.

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 annotations, no output schema, six parameters, and only 67% schema coverage, the description should explain return/behavior context (outputs, attached-instance semantics, model method effects). It instead spends its budget restating parameter roles already in the schema.

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 67%, so several parameters are already documented. The description's clarification of `dependent` vs `predictors` largely restates what the schema already says ('Dependent (outcome) variable'), adding only marginal value and no detail on `method`, `sheet`, or `path`.

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+resource: multiple linear regression via STATISTICA General Regression Models. It does not distinguish itself from siblings like statistica_anova or statistica_correlation, but the statistical operation is unambiguous.

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?

No when-to-use guidance, no prerequisites, no routing to alternatives such as statistica_correlation for association or statistica_anova for group comparison. The agent must infer that this is the tool for predictive modeling.

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