spss_mixed

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

No annotations are present, so the description bears the burden of behavioral disclosure. It adds one meaningful prerequisite (IBM SPSS Statistics installation) and notes supported structures, but does not disclose side effects, error behavior, or output specifics. The description largely repeats capabilities rather than providing behavioral detail.

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 and front-loads the purpose; every sentence adds relevant context about the tool's scope and prerequisite. 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?

For a 9-parameter tool with no schema descriptions and no annotations, this sparse description leaves significant gaps in how to invoke correctly. It covers supported model types but lacks parameter semantics and usage guidance, making it insufficient for an agent despite an output schema existing.

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%, so the description must compensate, but it only vaguely references random effects and repeated-measures structures. It does not explain required parameters like file_path, dependent, and fixed_effects, nor optional ones such as method, subject, covtype_random, and repeated_type.

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 uses a specific verb ('Run') and clearly identifies the tool as a linear mixed-effects model with random effects, including nested/crossed random effects and repeated measures. This distinguishes it from sibling analysis tools like spss_glm_univariate or spss_genlinmixed, even though it does not explicitly name them.

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 use for multilevel modeling with random effects and repeated measures, giving clear context. However, it provides no explicit when-to-use vs alternatives or exclusions, such as when to choose spss_genlinmixed for generalized models or spss_repeated_measures_anova for simpler repeated-measures designs.

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