Compare Study Runs

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

Annotations only indicate readOnlyHint=false and destructiveHint=false. The description goes further by disclosing a concrete side effect: 'Serving prediction evidence appends access audit events,' plus behavioral nuances like 'flagged, not ranked' and no model fitting/promotion. No contradiction with annotations.

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?

Three short sentences, each earning its place: main purpose first, side-effect disclosure second, exclusions third. No redundant phrasing or filler.

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 comparison tool with an output schema and three simple params, the description covers the core operation, constraints, side effects, and exclusions. Return-value details are presumably covered by the output schema, so nothing essential is missing for invoking the tool correctly.

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 adds useful constraints: run_ids are '2-20 candidates' and policy_id maps to 'one quality policy.' However, it does not explicitly define study_id or provide parameter-level semantics such as id formats or relationships, so compensation is partial.

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 first sentence states a specific verb and resource: compare 2-20 candidates against one quality policy. The closing sentence adds exclusions ('Does not fit or promote models'), which clearly differentiates it from model-lifecycle siblings like adopt_model_into_study and create_model.

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 clear context for when to use the tool (multi-candidate comparison against a quality policy) and states what it does not do (fit/promote models, rank different datasets). It does not explicitly name a sibling alternative or give an exact 'use X instead' condition, but the boundary is clear enough for routing.

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