rlassologit_effect

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

The `readOnlyHint` annotation already signals that this is a safe read-style operation, and the description adds the useful behavioral context that control selection is Lasso-based and the outcome is binary. It does not disclose details such as which inference quantities are returned or any computational caveats, but the output schema exists and the read-only annotation lowers the disclosure burden.

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?

A single, front-loaded sentence that packs in the treatment, outcome type, and control-selection method with no filler. Every phrase contributes meaning.

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 tool with 11 parameters, full schema coverage, an output schema, and a read-only annotation, the description supplies the one piece the schema lacks: the statistical role of the required inputs. It is largely complete for invocation, though a brief note on how it differs from `rlassologit_effects` would make it fully self-sufficient.

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 coverage is 100%, so the baseline is 3, but the description adds real semantic value by assigning roles: `d` is the treatment/exposure variable, `y` is the binary outcome, and `x` are the controls. This is especially helpful because the schema entry for `d` only says 'd parameter (Union...)' and does not explain the causal role that `d` plays.

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 identifies the estimation target — the effect of treatment `d` on binary outcome `y` while Lasso-selecting controls `x` — which goes well beyond a tautology and gives an agent the core model setting. It does not explicitly use a verb like 'estimates' and does not contrast itself with the closely related sibling `rlassologit_effects`, so it stops short of full sibling 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 intended setting is implied: binary outcome, a treatment variable `d`, and controls selected by Lasso. However, the description gives no explicit 'use this when...' guidance and names no alternative tools, even though siblings like `rlassologit`, `rlassologit_effects`, and `rlasso_effect` exist in the same family.

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