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

The description goes well beyond the annotations, disclosing upsert semantics, overwrite behavior, pass-clears-failures, OWNER/ADMIN authorization requirements, and downstream side effects on judge calibration and recalibration slices. It also specifies 201, 400, and 403 outcomes, giving an unusually complete behavioral picture.

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 substantial but every sentence earns its place: purpose, endpoint, auth, return shape, upsert behavior, side effects, and validation errors. It is front-loaded with the core purpose and organized with clear notes, avoiding redundancy with the schema.

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 mutation tool with no output schema, the description provides the return payload, auth requirements, side effects, and validation behavior. Combined with a fully documented input schema, an agent has everything needed to invoke the tool correctly and anticipate consequences.

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 schema already documents every parameter. The description adds meaningful behavioral semantics beyond the schema: re-labeling replaces the prior judgment, the newest wins, and a 'pass' clears prior failure attributions — all of which clarify how verdict, critique, and request_id interact.

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 precise verb and resource: recording a pass/fail verdict on a logged request or agent run, and explicitly links it to ground-truth judge calibration and training rewards. It is clearly differentiated from read-only siblings like list_labels and from pairwise eval labelling tools.

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 establishes clear context for when to use this tool: when a human or downstream system needs to record the authoritative verdict on a logged request. It explains scope choices and side effects, though it does not explicitly name alternatives or state when not to use it.

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