spss_glm_univariate

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the installation prerequisite and supported analysis features, but does not disclose side effects such as whether 'save_predicted' modifies data files, whether SPSS is launched in batch mode, or what operational constraints apply.

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?

The description is three short, purposeful sentences: action first, capabilities second, environment prerequisite third. It is front-loaded, avoids redundancy with the tool name, and contains 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?

For an 8-parameter statistical tool with no annotations and no schema descriptions, the description is too sparse to enable confident invocation. It leaves unclear how to specify variables, what file formats are expected, which parameters are optional or mutually exclusive, and how this GLM tool relates to nearby siblings like spss_anova.

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 description coverage is 0%, and the description only loosely hints at factors, emmeans, and posthoc parameters without explaining their formats or relationships. It adds no meaning for file_path, dependent, covariates, posthoc_method, or save_predicted, so parameter semantics remain largely underdocumented.

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 states a specific action ('Run univariate general linear model') and a clear scope ('factorial designs'), while mentioning distinctive capabilities like estimated marginal means, contrasts, and post-hoc tests. It does not explicitly differentiate from close siblings such as spss_anova or spss_manova, but the univariate/factorial framing provides adequate orientation.

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: for univariate GLMs with factorial designs and optional post-hoc/emmeans analyses. However, it gives no explicit when-not-to-use guidance or alternatives among the many SPSS analysis siblings, leaving routing largely to the agent's inference.

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