cluster_cate

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

Annotations only declare readOnlyHint=true and openWorldHint=false, so the description carries the burden of behavioral context. It adds substantive detail about assumptions (unconfoundedness, overlap, consistent nuisance estimation, cross-fitting), failure modes, and a minimum sample size, which goes well beyond the structured annotations. It does not describe output side effects or edge-case return behavior, but the output schema covers those.

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 well structured with labeled segments (Validation, Assumptions, Pre-conditions, Failure modes, Alternatives, Typical minimum N) that make it scannable. It is dense but every segment earns its place for a complex estimator, and the core purpose is front-loaded in the first phrase.

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 high-complexity tool with 12 parameters, 4 required fields, and an output schema, the description covers assumptions, preconditions, failure modes, alternatives, and minimum sample size. Nothing essential for an agent to decide whether and how to invoke this tool is missing.

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%, so the baseline is 3 even without extra parameter details in the description. The description's preconditions broadly map to required parameters (covariates, treatment, outcome, data) but do not add syntax or formatting guidance beyond the schema. It does not fully compensate for parameter meaning, but the schema already does that work.

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 opens with 'Cluster-based CATE estimator,' which clearly identifies the operation as estimating conditional average treatment effects and distinguishes it from sibling CATE/ML tools via the 'cluster-based' qualifier. It lacks an explicit verb-form statement like 'Estimates...' and relies on the noun phrase to convey purpose, so it stops 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?

The description provides pre-conditions, failure modes, typical minimum N, and explicitly names alternatives (sp.dml, sp.causal_forest, sp.tmle). It does not fully spell out when to choose cluster_cate over those alternatives, though the preconditions and failure-mode guidance make the intended context reasonably clear.

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