spss_logistic_regression

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the prerequisite that IBM SPSS Statistics must be installed and lists supported features, which is useful context. However, it does not disclose side effects, output format, error behavior, or whether it saves files. This is a moderate disclosure that could be richer, especially for a complex statistical procedure.

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 two sentences, front-loaded with the core action, and both sentences carry meaningful information. It avoids redundancy and fluff, making it concise and easy to scan. This is an example of efficient writing without irrelevant 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?

This is a complex tool with 8 parameters, no annotations, and an output schema that is not described in the text. The description covers only high-level capabilities and a prerequisite, leaving users without guidance on key parameters and usage context. For a statistical modeling tool, this is incomplete and likely to cause misconfiguration.

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 provides some meaning by tying 'stepwise selection' to the method parameter and 'categorical predictors' to the categorical parameter, but it completely ignores other parameters like contrast, print_options, and save_predicted. Given 8 parameters, this is insufficient guidance for an agent to correctly populate the 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 states the tool's function: 'Run binary or multinomial logistic regression.' This identifies the specific verb and resource, and the binary/multinomial qualifier helps distinguish it from ordinal or Cox regression. However, it does not explicitly contrast with sibling tools like spss_ordinal_regression or spss_cox_regression, so it stops short of full differentiation.

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 usage by mentioning support for stepwise selection, categorical predictors, and model diagnostics, suggesting scenarios like model building with categorical variables. But it provides no explicit when-to-use or when-not-to-use guidance, nor does it reference alternative tools such as ordinal or Cox regression. The intended usage is largely implicit.

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