spss_ordinal_regression

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

No annotations are provided, so the description bears the full burden. It mentions a runtime prerequisite and the parallel lines test capability, but it does not disclose side effects, whether data is modified, what happens with saved predictions, or what output behavior is expected beyond what the output schema might provide.

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 compact and front-loaded, with each sentence contributing meaningful information: what the tool does, what capabilities it has, and a key prerequisite. There is no filler or redundant restating of the tool name.

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?

The description covers the core purpose, typical use case, and a key external dependency, while an output schema exists to document return values. However, given a 7-parameter tool with no annotations and near-zero schema description coverage, it lacks guidance on parameter semantics, data prerequisites, and alternative tool selection, making it adequate but not fully complete.

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. It adds context for the 'link' parameter ('multiple link functions') and 'test_parallel' ('parallel lines test'), but required parameters like file_path, dependent, and predictors are not explained, leaving the agent to infer their meaning from names alone.

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 states a specific action ('Run ordinal regression (PLUM)') and the target scenario ('ordered categorical outcomes'), making the tool's purpose clear. It distinguishes itself from siblings like spss_logistic_regression by naming PLUM and the ordered-outcome use case, though it does not explicitly compare against alternatives.

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 phrase 'for ordered categorical outcomes' implies when this tool should be used, and the prerequisite 'Requires IBM SPSS Statistics to be installed' is a useful condition. However, it does not explicitly say when to prefer this over spss_regression, spss_logistic_regression, or spss_genlin, nor does it state when not to use it.

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