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Claim Check
x402
Claim Check is a paid API for AI agents from claim-check.agent-utils.workers.dev, paid per call via x402, $0.05/call, status unknown (last checked 2026-09-15).
Fact-checks a claim against a source document or URL with verbatim quote grounding, returning a verdict, confidence score, and supporting excerpts.
Price / call$0.05
Success—
StatusUnknown
How this service works
Grounded verification and extraction for AI agents: fact-check a claim against a source with verbatim quotes, or turn invoices, receipts, job posts and any text into typed JSON. Pay per call over x402, no API key. Daily model quota published at /health.
Output
A JSON object containing: the original claim, a verdict (supported/contradicted/not_found/unclear), a confidence score between 0 and 1, an array of verbatim quotes from the source that ground the verdict, a plain-language explanation, the source URL if provided, character count of the source, count of grounded vs dropped quotes, and the model name used.
Example use cases
Verifying AI-generated facts against sources
“I have an AI-written paragraph that says 'GPT-4 was released in March 2023' — can you check that claim against https://en.wikipedia.org/wiki/GPT-4 and tell me if it's supported, contradicted, or not found, along with any exact quotes?”
Auditing invoice line items
“Here's the text of an invoice — can you verify whether it actually contains a line item for 'consulting services at $150/hr' and pull out the exact quote if it does?”
Checking job posting requirements
“I want to know if this job posting actually says candidates need 5 years of Python experience — here's the full text, please verify that claim and give me a supported/contradicted/not-found verdict with the relevant quote.”
Technical details
Endpoint
POSThttps://claim-check.agent-utils.workers.dev/v1/claim-checkWhen to prefer
Choose this endpoint when you need grounded, quote-backed fact verification rather than open-ended LLM opinion — it discards any model-generated quote not found verbatim in the source, making results auditable. Prefer it over generic LLM calls when you need a structured verdict enum and confidence score for downstream logic, or when you need to extract typed structured data from invoices, receipts, or job postings without building a custom extraction prompt. It is particularly valuable when source provenance and citation traceability matter.
Side effects
- Source URL is unreachable or returns non-200 — fetch fails and an error is returned
- Source text exceeds 200,000 characters — truncated or rejected
- Claim is ambiguous — verdict returned as 'unclear' with low confidence
- Model quota exhausted for the day — 402 or quota error from /health endpoint
- Verbatim quotes hallucinated by model are discarded (droppedQuotes > 0), reducing grounded evidence
- Payment not included or invalid — x402 payment required error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"claim": "The Eiffel Tower is 330 metres tall.",
"model": "@cf/meta/llama-3.3-70b-instruct-fp8-fast",
"quotes": [
"The tower is 330 metres (1,083 ft) tall"
],
"modelMs": 1800,
"verdict": "supported",
"sourceUrl": "https://en.wikipedia.org/wiki/Eiffel_Tower",
"confidence": 0.95,
"explanation": "The source states the tower's height as 330 metres.",
"sourceChars": 24000,
"droppedQuotes": 0,
"groundedQuotes": 1
}
}Tags
x402
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