statistica_anova

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

No annotations are supplied, so the description carries the full burden. It does disclose the return artifacts (ANOVA table / UnivariateResults plus parameter estimates), which is genuine behavioral information, but says nothing about side effects, whether the live workbook is modified, or what happens when `attach` is used.

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 short sentences, front-loaded with method and module, then the key parameter meanings, then the return value. No filler or repetition; every sentence carries information.

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 6-parameter analysis tool with no annotations and no output schema, the description covers the essentials of what comes back but leaves the file/sheet parameters, side effects on the running STATISTICA instance, and model-building defaults to inference from the schema alone.

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%; `attach` and `method` are already documented in the schema, and the description's gloss on `dependent` ('the outcome') and `between` ('factor/covariate effects') largely restates the schema while adding the covariate nuance. `path` and `sheet` remain undocumented in both places.

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 statistical verb and resource (analysis of variance via General Linear Models, module 4100) and names the key variables. It is readily distinguishable from siblings such as statistica_regression, statistica_t_test and statistica_correlation without opening any 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 only implied by the statistical method name: an agent that knows ANOVA infers it is for comparing means across factor levels. The description never states when to prefer it over statistica_t_test or statistica_regression, and gives no prerequisites or exclusions.

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