Compare Study Runs

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

Far beyond the annotations, the description discloses crucial behavior: rows are comparable only when multiple dimensions match, incompatible rows are returned with the differing dimension in blockers and are never ranked, and serving prediction evidence appends access audit events. It also clarifies that no model fitting or promotion occurs, adding meaningful non-obvious context.

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 dense but purposeful, with the core purpose front-loaded and supporting behavior details following. It is long, though each clause adds real value; a slight structural breakdown could improve readability, but no sentence is wasted.

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 that an output schema exists, the description need not explain return values, and it does a strong job explaining behavior, exclusions, and ranking semantics. The main gap is that study_id's meaning is left implicit, and since the schema gives no parameter descriptions, that missing piece prevents full completeness.

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 carries the burden. It implicitly explains run_ids as the 2-20 candidates and policy_id as the quality policy, but it never mentions study_id, its role, or any constraints on it. The description partially compensates for the bare schema but does not fully document all three required parameters.

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 a specific verb and resource: 'Compare 2-20 candidates against one quality policy,' which is unambiguous about what the tool does. It further defines the output rows, comparability conditions, and explicitly scopes the tool to predictive ranking, making it distinguishable from nearby siblings like evaluate_study_run or assess_study_validation_pair even without naming them.

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 clearly states when to use the tool ('answers predictive ranking only') and gives explicit exclusions: sensitivity agreement is not computed, analyst acceptance lives in decisions, business validity is a human review, and it does not fit or promote models. It does not name alternative sibling tools, but the when-not guidance is strong enough for an agent to route correctly.

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