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Toll402 LLM-as-Judge
x402
Toll402 LLM-as-Judge is a paid API for AI agents from toll402.dev, paid per call via x402, $0.07/call, status unknown (last checked 2026-09-14).
Scores a candidate output against a task and criteria (0–100) using an independent LLM judge, returning pass/fail, per-criterion scores, issues, and improvement suggestions.
Price / call$0.07
Success—
StatusUnknown
How this service works
Independent LLM-as-judge: scores a candidate output against a task and criteria (0-100), with pass/fail, issues and suggestions. Use it for self-verification, best-of-N, or QA gates.
Output
Returns a JSON object with an overall integer score (0–100), a boolean pass/fail verdict, an array of per-criterion scores each with criterion name, integer score, and rationale, a list of identified issues, and a list of improvement suggestions. Also includes elapsed time in milliseconds and the tool name used.
Example use cases
QA gate before agent publishes output
“Before my agent sends this customer support reply, judge it against the task 'Answer the customer's billing question politely and accurately' — score it on correctness, completeness, and tone, and flag any issues so I can decide whether to publish it.”
Best-of-N candidate selection
“I have two AI-generated summaries of a legal document. Can you score each one against the task 'Summarize this contract's key obligations in plain English' on correctness and clarity, so I can pick the better one?”
Self-verification in agentic pipeline
“My pipeline just produced this SQL query as an answer to 'Write a query that returns all users who signed up in the last 30 days and have made at least one purchase.' Score it 0–100 on correctness and completeness, and tell me whether it passes and what to fix if not.”
Technical details
Endpoint
GEThttps://toll402.dev/v1/judgeWhen to prefer
Choose this endpoint when you need an independent, structured numeric judgment of an LLM output rather than just a binary check — especially for self-verification loops, best-of-N selection, or QA gates in agentic pipelines. It is particularly useful when you want per-criterion breakdowns with rationale and actionable suggestions, rather than a simple pass/fail or raw similarity score.
Side effects
- Missing required 'task' or 'candidate' query parameters returns a validation error
- Candidate or task text exceeding 100,000 or 20,000 character limits respectively causes rejection
- Criteria array with fewer than 1 or more than 10 items returns a schema validation error
- Network timeout or upstream LLM unavailability may return a 5xx error
- Ambiguous or very short task descriptions may produce unreliable or low-confidence scores
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"pass": true,
"issues": [],
"scores": [
{
"score": 90,
"criterion": "is a haiku (5-7-5)",
"rationale": "..."
}
],
"overall": 88,
"suggestions": []
}
}Tags
x402
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