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set judge settings

set_judge_settings
Idempotent

Set or clear the workspace's default judge model for eval runs that don't specify one. Revert to the house default by clearing the setting.

Instructions

Set (or clear) the workspace's default judge model for eval runs that don't specify one — must be a chat model from the platform catalog offered to this workspace. PUT /v1/settings/judge (API-key scope: platform:write). Returns: 200 { default_judge_model: string|null, house_default: string } — the settings after the update. Notes: Judges run on the platform's key and are metered to the wallet, so only platform-catalog models qualify (a workspace's own provider keys are for inference, not judging). Needs platform:write (it is a workspace setting).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
default_judge_modelNoModel id (e.g. "openai/gpt-4.1") to use as the default judge, or null / omitted / empty string to revert to the house default. Any non-string, non-null value → 400 'default_judge_model must be a string or null'. Must be a chat (non-embedding) model this workspace is offered, otherwise 400 '"<model>" isn't a chat model this workspace is offered.'
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TDQS

A4.5/5.0
Behavior5/5

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

The description discloses the PUT endpoint, required API-key scope (platform:write), return payload shape, and the metering/wallet behavior. It also explains the 'clear' semantics via null, which complements the idempotentHint annotation without contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core action, followed tightly by endpoint, return shape, and rationale. Every sentence contributes essential information with no filler or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With one optional parameter and no output schema, the description covers all necessary call context: auth scope, model constraints, return shape, and clearing behavior. An agent has everything needed to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the single parameter is thoroughly documented in the schema including null/omitted/empty-string behavior and error cases. The description adds context about what the judge model is for, but no new parameter semantics beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Set (or clear)') and resource ('workspace's default judge model for eval runs that don't specify one'), making the tool's function immediately clear. It is easily distinguishable from the sibling get_judge_settings and other model-related tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly states when to use the tool (for eval runs that don't specify a judge) and what models qualify (chat models from the platform catalog offered to the workspace). It also clarifies why provider keys cannot be used, though it does not name alternative tools or exclusions.

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

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