spss_mixed

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

With no annotations provided, the description carries the full burden of disclosing behavior. It mentions support for nested/crossed random effects and repeated measures structures, but does not describe side effects, output format, error conditions, or whether any data is modified. The only operational detail is the requirement that SPSS be installed.

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 concise and front-loaded with the core action. It wastes no words and adds meaningful context about the type of model and prerequisite. It is appropriately sized for a tool of this complexity, though it could be structured slightly better by separating capabilities from requirements.

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?

This is a complex model-fitting tool with 9 parameters, no annotations, and zero schema coverage. The description does not explain how to specify random effects, fixed effects, subject/repeated terms, or covariance structures, making it difficult for an agent to invoke correctly without additional external knowledge. The output schema exists but cannot compensate for missing parameter and usage context.

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 mention any of the 9 parameters, including the required ones like file_path, dependent, and fixed_effects. The description must compensate for the lacking schema documentation but does not, leaving an agent with no semantic understanding of how to populate the fields.

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 specific action ('Run') and the resource ('linear mixed-effects model'), and further clarifies it as a multilevel model with random effects. This distinguishes it from many siblings that run other analysis types, such as spss_anova or spss_regression.

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 provides no explicit guidance on when to use this tool versus close alternatives like spss_genlinmixed, spss_repeated_measures_anova, or spss_glm_univariate. It implies use for multilevel/repeated measures data, but does not state when not to use it or what distinguishes it from these siblings.

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