athena_causal_aipw

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

With no annotations, the description must disclose behavior, and it does reveal the method (cross-fitting, influence functions) and inferential outputs (SE, CI). However, it does not explain key behaviors such as required data assumptions, handling of missing data, or limitations of the linear/logistic nuisances. The phrase 'Assumption-scoped' hints at assumptions without specifying them.

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 brief and front-loaded, with two sentences that pack technical detail. However, 'Assumption-scoped' is vague and contributes little actionable information, so it's not perfect.

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 complex causal inference tool with 6 parameters (including nested objects), no output schema, and no annotations, this description is materially incomplete. It omits data format expectations, parameter details, and concrete assumption statements, leaving the agent to guess.

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 0%, so the description should explain parameters, but it only indirectly references binary treatment and logistic propensity. It does not clarify what samples, adjustment, assumptions, or propensity_clip mean, nor the structure of the samples objects.

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 a cross-fitted AIPW estimator for binary treatment, specifying the outcome nuisance model (linear), propensity model (logistic), and inference via influence-function SE/CI. This differentiates it from sibling TMLE tools, though the verb 'estimate' is implied rather than explicit.

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 offers only implicit usage constraints: it is for binary treatment and uses specific model forms. It does not say when to prefer AIPW over alternatives like TMLE, nor does it state exclusions or assumptions in concrete terms ('Assumption-scoped' is vague).

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