list label sets

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds valuable behavioral detail beyond annotations: the exact endpoint, API-key scope, bare-array response format instead of an envelope, newest-first ordering, and precise null semantics for kappa. No contradiction.

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 somewhat long but every sentence earns its place: purpose, endpoint/scope, complete return shape, and caveats are all separated clearly. The critical 'bare array' warning is isolated in a Notes section rather than buried.

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?

With no output schema, the description fully compensates by enumerating every returned field and its type/null behavior. It also covers ordering, endpoint, scope, and the kappa edge case, making the tool callable without any external documentation.

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?

The tool has zero parameters and the schema is an empty object with 100% coverage, so there is no parameter semantics to add. The rubric's baseline of 4 applies, and the description instead enriches the response semantics extensively.

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 uses a specific verb ('List') and a specific resource ('the workspace's golden sets') while defining what golden sets are. It also clarifies the distinguishing scope ('named collections of human-graded requests...') so it will not be confused with list_labels or the label-set management siblings.

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 explicitly frames when to use the tool: 'use it to see which sets exist, which are frozen, and which judges calibrate on them.' It gives clear practical context though it does not name alternatives or state when not to use it, so it stops short of a 5.

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