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create criterion

create_criterion

Create a judge criterion to score traffic automatically and calibrate it against human labels. Define the rubric, judge model, and population for reliable evaluations.

Instructions

Create a judge criterion (a rubric prompt run by a judge model) that can score traffic online and be calibrated against human labels. POST /v1/criteria (API-key scope: evals:write). Returns: 201 with the criterion object (same shape as list items): id, name, description, judge_prompt, judge_model, status, source, unit, population, population_family, online_* fields, tier, trust, ci, drift, tpr, tnr, kappa, alignment_n, aligned_at, created_at Notes: OWNER/ADMIN only (403). Creating does not spend; judging (align, online monitoring) does. Body keys are snake_case exactly as listed; other criterion knobs (coverage, pre-checks, contract rules) are not settable through this endpoint.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesUnique within the workspace, trimmed, 1..80 chars. Duplicate name is a 400.
unitNoWhat one verdict covers: "request" judges one exchange, "trace" judges a whole agent run. CREATE-ONLY; cannot be changed later. Default: "request".
populationNoRequest tag this criterion judges online AND calibrates against (one binding). Max 64 chars; "" = all traffic.
descriptionNoOptional note, max 500 chars (nullable).
judge_modelYesModel id that runs the judgment. Must be an available model (400 "Judge model '<id>' is not available.").
judge_promptYesThe rubric the judge model applies, trimmed, 10..4000 chars.
population_familyNoAuto-detected traffic segment (a `family` value from GET /v1/logs facets, 16 hex chars or "none") scoping the same binding. Max 32 chars; "" = no segment scope.
Install Server

TDQS

A4.4/5.0
Behavior5/5

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

Beyond annotations, the description discloses the HTTP method, required permission (OWNER/ADMIN, 403), side-effect distinction (create does not spend vs judging spends), and the 201 response shape. It also states endpoint limitations on settable fields. 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.

Conciseness4/5

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

Front-loaded purpose followed by endpoint, response, and notes in a compact structure; the response field enumeration is long but compensates for the missing output schema. Every clause adds information, though the single paragraph is dense.

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?

Despite no output schema, the description fully specifies the return payload, auth requirements, spend implications, and keyword casing. Combined with the 100%-covered input schema, an agent has everything needed to invoke the endpoint 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 covers all 7 parameters with individual descriptions (100% coverage), so the description need not redefine them. It adds only a snake_case serialization note and an exclusion of other knobs (coverage, pre-checks, contract rules), which is helpful but marginal semantic value.

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?

Description opens with a precise verb+resource: 'Create a judge criterion' and defines it as 'a rubric prompt run by a judge model' that scores traffic online and calibrates against human labels. It includes the HTTP endpoint and API-key scope, making the operation unmistakable and distinct from sibling criteria/eval 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?

Notes give concrete usage context: OWNER/ADMIN only, POST /v1/criteria with evals:write scope, and 'Creating does not spend; judging (align, online monitoring) does.' It also warns that 'other criterion knobs ... are not settable through this endpoint,' but does not explicitly name an alternative tool like update_criterion or suggest_criteria.

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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