spss_discriminant

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

With no annotations provided, the description adds some behavioral context by noting the IBM SPSS requirement and supported options (stepwise selection, cross-validation). However, it does not disclose potential side effects such as creating new variables or modifying the input file, or any permissions or error behaviors.

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?

Two sentences that are lean and front-loaded: the first states the core purpose, the second adds key capabilities and a prerequisite. No wasted words 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 complex statistical tool with 8 parameters and an external dependency, the description covers the essential identity and requirement but omits details like data format expectations, grouping variable constraints, or output behavior. The presence of an output schema lessens the need to describe return values, but assumptions and preconditions are missing.

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 by explaining parameter semantics, but it does not. It only hints at stepwise selection, which maps vaguely to the method enum, but leaves parameters like priors, group_values, and save_class unexplained. The description adds minimal value over the raw schema.

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 specifies the tool's function: 'Run discriminant analysis to classify cases into groups.' This is a specific verb+resource statement that distinguishes it from sibling tools like regression or factor analysis. Mentioning stepwise selection and cross-validation further clarifies scope.

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 when discriminant analysis is needed for classification, but it does not explicitly contrast with alternatives like logistic regression or MANOVA, nor does it provide when-not-to-use guidance. The context is clear enough for a basic selection but lacks explicit exclusions.

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