spss_discriminant

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

No annotations exist, so the description must carry the behavioral transparency burden. It discloses an external dependency and mentions stepwise/cross-validation support, but it does not state whether the tool modifies files, what outputs it produces, or any side effects/errors. This is insufficient for an 8-parameter statistical 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 short sentences, all front-loaded and free of filler. Each sentence adds some useful signal (action, capabilities, prerequisite), so it scores high on conciseness despite being light on detail.

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 complex tool with 8 parameters, no annotations, and no parameter documentation, this description is not sufficient to guide correct invocation. The output schema exists but does not compensate for missing parameter semantics, usage conditions, and behavioral caveats; the description needs additional 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%, so the description must compensate, but it only loosely maps to parameters: groups/predictors via the classification phrasing and stepwise via the method enums. It leaves group_range, priors, save_class, save_scores, and file_path unexplained.

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 identifies the exact analysis ('discriminant analysis') with its goal ('classify cases into groups'). It is clear and, by naming the classification purpose, helps set it apart from related statistical tools such as regression or MANOVA, though it does not explicitly name an alternative.

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?

No guidance is given on when to choose discriminant analysis over sibling tools (e.g., logistic_regression, manova) or which method/enum to select for stepwise vs. direct entry. The only operational context is the external SPSS prerequisite, which is useful but not usage direction.

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