rlasso_effect

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

The annotations already declare readOnlyHint=true, so the agent knows this is a safe operation. The description adds one meaningful behavioral detail: Lasso-based control selection happens before estimating the effect. It does not disclose estimator assumptions, required preprocessing, or edge-case behavior, but with annotations covering the safety profile, this is acceptable.

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 front-loaded sentence with no filler, efficiently communicating the core purpose. It is appropriately terse, though it omits potentially useful context such as estimator family or relationship to sibling tools.

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?

With 14 parameters but only 3 required, detailed schema descriptions, an output schema, and read-only annotations, much of the invocation detail is covered elsewhere. However, the description leaves ambiguity about whether this tool handles a single treatment effect versus multiple effects (as in `rlasso_effects`) and what model family is assumed, so it is not fully complete for an agent selecting among near-identical siblings.

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 100%, which establishes a baseline of 3. The description adds role semantics by mapping `d` to the treatment, `y` to the outcome, and `x` to the controls selected by Lasso, which is more informative than the schema's generic phrasing such as 'd parameter' and 'Primary running variable, regressor, or feature input.'

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 purpose: estimating the effect of `d` on `y` while using Lasso to select controls from `x`. This is clear and non-tautological. It does not explicitly distinguish itself from near-identical siblings like `rlasso_effects` or `rlassologit_effect`, so it falls short of a 5.

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 given about when to prefer this tool over alternatives such as `rlasso_effects`, `rlassologit_effect`, `rlasso_iv`, or `lasso_select`. The phrase 'after Lasso-selecting controls' weakly implies a high-dimensional-control setting, but there is no explicit when/when-not routing or mention of alternative tools.

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