spss_glm_univariate

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only mentions the requirement that IBM SPSS Statistics be installed, but does not disclose what the tool returns, whether it modifies files, or any side effects. This is a significant gap for a statistical computation 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?

The description is two sentences long, front-loaded with the core purpose, and every sentence adds value. It efficiently states what the tool does and a key requirement without any filler or repetition.

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?

Given the complexity of 8 parameters and no schema descriptions, the description is too minimal. It does not provide enough context for an agent to decide when to choose this over spss_anova, how to specify the model, or what the output shape will be. The tool's integration in a broader SPSS suite requires more guidance.

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 does not compensate. It mentions 'estimated marginal means' and 'post-hoc tests' which map to emmeans and posthoc parameters, and 'contrasts' which is not a parameter in the schema. It does not explain core parameters like file_path, dependent, factors, covariates, or save_predicted, leaving the agent to infer their meaning from names alone.

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 states the tool 'Run univariate general linear model (GLM) with factorial designs,' using a specific verb and resource. It distinguishes itself from sibling tools like MANOVA or ANOVA by specifying 'univariate' and 'factorial designs,' making its purpose 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?

The description implies usage for factorial designs and lists supported features (estimated marginal means, contrasts, post-hoc tests), giving clear context for when to use it. However, it does not explicitly name alternatives or exclusion criteria, such as preferring spss_anova for simpler models, so it misses a point.

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