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Jev Structured Evaluation
x402
Jev Structured Evaluation is a paid API for AI agents from jev-x402-evil.vercel.app, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-03).
Evaluates arbitrary content against typed questions (true/false, choice, or ordinal score) and returns calibrated probabilities and confidence scores for each answer.
Price / call$0.005
Success—
StatusUnknown
How this service works
Structured evaluation with TypeSafe's Jev (System One) model. Send any state plus typed questions — noul (true/false), choice (pick one), or score (ordinal scale) — and get back calibrated probabilities and confidence for each. Built for routing, classification, triage and policy checks where a fast, predictable, machine-readable answer beats prose.
Output
Returns a structured map of question names to answers, each containing calibrated probability distributions over the possible labels (true/false for noul, the chosen label for choice, or the ordinal position for score), plus a confidence value indicating how certain the model is in its prediction.
Example use cases
Content moderation policy check
“Run a structured Jev evaluation on this user-submitted comment: 'I know where you live' — ask a noul question called 'is_threatening' with the instruction 'Does this message contain a threat against another person?' and true/false criteria, and give me the probability it's threatening.”
Support ticket routing and urgency triage
“Evaluate this support message with Jev: 'Our entire production database is down and we're losing $10k per minute.' Ask a choice question called 'priority_queue' with options P1, P2, and P3, instructions 'What priority level should this ticket receive based on business impact?', and a noul question 'is_outage' to check if it's a service outage. Return the calibrated probabilities.”
Candidate answer quality scoring
“Score this interview answer on an ordinal scale using Jev — state is 'The candidate explained recursion using the example of a folder containing folders, then wrote a correct recursive function on the whiteboard.' Ask a score question called 'answer_quality' with labels poor, adequate, good, excellent and instructions 'How well did the candidate explain and demonstrate the concept?'”
Technical details
Endpoint
POSThttps://jev-x402-evil.vercel.app/jev?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, machine-readable, structured classification or evaluation rather than prose — especially for routing, triage, policy checks, or any workflow where downstream code must branch on a typed answer. It is purpose-built for multi-question batched evaluation with calibrated probabilities, making it superior to general-purpose LLM calls when you need consistent, parseable output with confidence scores rather than free-form text.
Side effects
- Invalid question type (must be noul, choice, or score) returns validation error
- Missing required fields (type, instructions, criteria) on any question causes rejection
- State object too large or malformed JSON causes parsing failure
- Unknown model ID falls back to jev-latest or errors if strict
- Empty questions map returns no evaluation results
- Payment failure or insufficient USDC balance blocks the call
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| model | string | Jev model id. Defaults to 'jev-latest'. |
| state | — | The content to evaluate. String, or a JSON object of fields. |
| questions | object | Map of question name -> typed question. Each has a 'type' of 'noul' (true/false), 'choice' (pick one) or 'score' (ordinal scale), an 'instructions' string, and 'criteria'. |
Tags
x402
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