set_prespecified_confounders

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

With no annotations, the description fully discloses behavioral traits: the blocking nature, the definition of 'important', and the ratification queue behavior ('the assessment then carries P1 in its ratification queue and is explicitly not final until they do'). It also clarifies scoping by review_id, adding meaningful context beyond the schema.

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 longer than typical but every paragraph adds necessary context: purpose, blocking, importance definition, scoping, and ratification. It is well-structured and front-loaded with the core action, though it could be slightly tightened without losing key information.

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?

Given the tool's complexity, no annotations, and an output schema present, the description is complete. It covers when to use, why it's blocking, how to handle ratification, and scoping. With no annotations, it fully carries the burden of behavioral and contextual explanation.

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 0%, so the description must compensate. It explains 'confounders' (the list of important factors), 'review_id' (scoping to a review), and 'ratified_by' (human acceptance). Only 'rationale' is not mentioned, but it's optional with a default, so the compensation is strong.

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 states a specific action ('Supply P1') and the resource (confounding factors for a review) with context ('before any study is assessed'). It distinguishes itself from per-study tools by emphasizing that P1 belongs to the review, not a single study.

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?

Explicitly states when this must be used ('THIS IS BLOCKING. Domain 1 will not be scored without it'), what counts as important confounders, and how to handle ratification (leave ratified_by empty unless a human accepts). It also warns against substituting the paper's covariate list, providing a clear when-not.

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