rlassologit_effects

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is known. The description adds no behavioral context beyond the modeling intent: it does not mention prerequisites (e.g., a fitted model), whether it trains a model internally, how missing data is handled, or what 'targeted column' implies for the operation. With annotations present, the bar is lower, but the description still fails to disclose anything beyond the basic purpose.

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 a single, dense sentence with no wasted words. It front-loads the core intent. Minor issue: the technical jargon ('Logistic high-dimensional') may obscure meaning for some agents, but the structure itself is efficient.

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?

Despite having an output schema and annotations, the description leaves the tool's purpose and invocation ambiguous. It does not specify what 'targeted column' means (how are targets chosen?), how this tool differs from rlassologit_effect, or what the returned effects represent. Given the tool's complexity (11 parameters) and the wide sibling list, this one-line description is insufficient for an agent to plan a correct call.

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?

The input schema has 100% description coverage, so all 11 parameters are already documented. The description does not add meaning beyond the schema–it only references X generically and does not explain how 'targeted columns' are specified or which parameters control the effect estimation. Baseline 3 is appropriate because the schema carries the semantic load.

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 identifies the resource ('each targeted column of X') and the analysis family ('Logistic high-dimensional effect'), which is specific enough to distinguish it from general regression tools. However, it uses the noun 'effect' rather than an explicit verb like 'computes' or 'estimates', and does not mention the relationship to the similarly named sibling rlassologit_effect or rlassologit, leaving the exact scope slightly ambiguous.

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 provided on when to use this tool versus its alternatives (rlassologit, rlassologit_effect) or under what conditions it is appropriate. The description neither states when to choose this tool nor excludes situations where a different tool would be better. An agent is left to infer usage from the name alone.

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