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create_eval_criterion

Create a custom evaluation criterion with name, rubric, and score range. Choose between LLM-based scoring or deterministic evaluators for instant results.

Instructions

Create one custom eval criterion in your account (Pro+ only). name + rubric + scaleMin + scaleMax are required. Same name already existing in the account = 409. A name matching a global default is structurally allowed (UNIQUE (account_id, name) separates it from account_id IS NULL). type defaults to 'llm_judge' (judge LLM scoring). Specifying a deterministic evaluator type (exact_match / contains / regex / json_schema / json_path) scores without calling an LLM — free and instant (pass -> scaleMax / fail -> scaleMin). Deterministic types require config. The path an AI agent takes when it decides "add this criterion" during dogfood evals.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesCriterion name (1-50 chars, starts with an alphanumeric, [A-Za-z0-9 _\-.] only). E.g. 'helpfulness' / 'concise'
typeNoEvaluator type (default 'llm_judge'). Deterministic evaluators score without an LLM call — free and instant: 'exact_match' / 'contains' / 'regex' / 'json_schema' / 'json_path'
scopeNoEvaluation scope (default call). call = per call; trajectory = scores multiple calls + steps in the same trace as one trajectory (llm_judge only)
configNoType-specific settings (not needed for llm_judge). exact_match: {expectedOutput}, contains: {substring, caseSensitive?}, regex: {pattern, flags?}, json_schema: {schema}, json_path: {path, expectedValue?}. Categorical scoring also requires config.categories (2-10 entries, worst to best).
rubricYesScoring rubric text (10-2000 chars; the narrative the judge LLM bases scores on. Required as a human-readable explanation even for deterministic evaluators)
scaleMaxYesScore upper bound (1-100, must be greater than scaleMin)
scaleMinYesScore lower bound (1-100, must be less than scaleMax)
scoreTypeNoScoring type (default numeric). boolean = pass/fail; categorical requires config.categories (llm_judge only)
Behavior4/5

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

With no annotations, the description discloses default type/scope, error conditions, and deterministic evaluator behavior (free/instant). It does not detail rate limits or auth but covers key behavioral traits.

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

Conciseness3/5

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

Multiple sentences, some verbose (e.g., final sentence about AI agent path). Core purpose is front-loaded but could be tightened. Still organized and informative.

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

Completeness4/5

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

Given 8 params, no output schema, and nested config, the description covers required fields, constraints, types, defaults, error behavior, and deterministic evaluator details. Missing return format but otherwise complete.

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

Parameters4/5

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

Schema coverage is 100%, baseline 3. Description adds context: deterministic types require config, rubric needed even for deterministic, examples for config fields, and default type/scope.

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 clearly states 'Create one custom eval criterion' and specifies the scope 'in your account (Pro+ only)'. It distinguishes from siblings like get_eval_criterion, list, update, delete by focusing on creation.

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 mentions required fields, uniqueness constraint causing 409, and when to use deterministic types vs llm_judge. However, it does not explicitly contrast with propose_eval_criteria or other creation tools.

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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