Spss Mixed

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

With no annotations, the description carries the full behavioral burden, and it only discloses one operational prerequisite ('Requires IBM SPSS Statistics to be installed'). It says nothing about whether the tool writes files, how convergence/failure is handled, what preprocessing the data needs, or roughly what it returns — significant gaps for a 9-parameter modeling 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?

Three tight sentences, front-loaded with the model type, followed by supported structures and the runtime dependency. No filler, though the capability sentence and the installation sentence are the only substantive content.

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?

An output schema exists, so return values need not be explained. But for a 9-parameter statistical tool with zero annotation coverage and zero schema descriptions, the definition leaves too much undefined — no parameter guidance, no usage context, and no behavioral detail beyond the SPSS dependency.

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% across 9 parameters, so the schema documents only names, types, and one enum, leaving the description to compensate — and it largely does not. Terms like 'random effects' and 'repeated measures structures' loosely gesture at random_effects/repeated, but there is nothing on file_path, dependent, fixed_effects, subject, method (REML vs ML), repeated_type, or covtype_random.

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 and resource ('Run linear mixed-effects model (multilevel model) with random effects') and names supported structures (nested/crossed, repeated measures). An agent can identify it as the mixed-modeling tool. It does not, however, explicitly route against close siblings like spss_genlinmixed, spss_repeated_measures_anova, or spss_glm_univariate.

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?

There is no when-to-use or when-not-to-use guidance, and no alternative is named. The capability phrases ('supports nested and crossed random effects') hint at applicable data shapes but never tell the agent when this tool is the right choice over the repeated-measures ANOVA or GENLINMIXED siblings.

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