create label set

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

Annotations already mark this as a non-read-only, non-idempotent write operation, and the description adds substantial behavioral context beyond that: the HTTP endpoint, API-key scope, exact 201 response shape, the 'NOT frozen yet' state, minimum/maximum size limits, verifier-source exclusion, and the subsequent freeze step. This gives the agent a thorough understanding of side effects and constraints.

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 well-structured, with the core action front-loaded followed by endpoint, return shape, constraints, and next steps. Every sentence carries actionable information; the explicit response object and error-message snippets earn their place, and the 'Notes:' section keeps constraints scannable.

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 supplies the full 201 return object; with only sparse annotations, it also provides endpoint, auth scope, size limits, verifier exclusion, and the follow-up freeze workflow. Combined with 100% schema coverage for parameters, an agent has everything needed to invoke this tool correctly and interpret its result.

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 coverage is 100%, so the baseline is 3, but the description adds meaningful behavioral semantics: it explains the conceptual distinction between 'explicit request ids or the newest N grades', reinforces that verifier-sourced labels never count, and highlights the minimum distinct graded requests requirement. These details clarify how to choose and validate parameter values beyond the schema text.

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 and resource ('Create a golden set from human-graded requests') and clearly distinguishes this tool from siblings like freeze_label_set and attach_label_set by emphasizing the set is 'NOT frozen yet' and must be frozen before attaching. It is immediately clear what the tool produces and where it fits in the workflow.

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 sequencing context — this is the 'first step' toward a frozen calibration set, and 'freeze the set before attaching it to a criterion' — so an agent understands when to use it relative to freeze_label_set and attach_label_set. It does not explicitly name alternative tools or give direct when-not-to-use conditions, but the workflow guidance is strong.

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