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SiteCheck Text Classifier
x402
SiteCheck Text Classifier is a paid API for AI agents from api.sitecheck-api.workers.dev, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-28).
Classifies one or more text strings against a user-supplied set of labels, returning a confidence score, reason, and excerpt for each result
Price / call$0.002
Success—
StatusUnknown
How this service works
Labels up to 20 texts with 2 to 20 labels you choose, with a confidence from 0 to 1 and a one-line reason per text. One label per text, or several with multi true. Uses the chat model with JSON output; labels are as good as the model's reading of them.
Output
A JSON object containing the model name used, the data source, and a results array where each item includes the text index, an array of matched labels with confidence scores, a natural-language reason for the classification, and an excerpt from the original text. Also includes a checkedAt ISO timestamp.
Example use cases
Support ticket triage by intent
“I have these five support messages — classify each one as 'billing issue', 'technical problem', 'feature request', or 'general inquiry' so I can route them to the right team: [list of messages]”
Product review sentiment labeling
“Classify each of these 10 product reviews as 'positive', 'negative', or 'mixed', and for each one give me the confidence score and a short excerpt that explains why: [list of reviews]”
Multi-label content moderation
“Check whether this user post contains any of the following: 'spam', 'hate speech', 'misinformation', or 'safe content' — it might match more than one label, so enable multi-label mode: 'Buy cheap followers now! Also, vaccines cause autism.'”
Technical details
Endpoint
POSThttps://api.sitecheck-api.workers.dev/api/classify?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need zero-shot text classification with confidence scores and reasoning against a custom label set you define at call time, without training a model. Ideal for dynamic label sets that change per request, or when you want explainability (reason + excerpt) alongside the label. Prefer over embedding-based classifiers when you want human-readable rationale, and over fine-tuned models when labels are ad hoc or low-volume.
Side effects
- Fewer than 2 or more than 20 labels provided — validation error
- More than 20 texts submitted at once — exceeds batch limit
- Empty or missing text/texts fields — returns error or empty results
- Ambiguous or overlapping labels may produce lower confidence scores
- Model unavailability on Workers AI falls back to Groq; if both fail, returns 5xx
- Payment not included or insufficient — x402 payment required error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | One text (or texts) |
| multi | boolean | true when a text may take several labels |
| texts | array | Up to 20 texts |
| labels | array | 2 to 20 different labels |
Output
Schema · Response
{
"type": "json",
"example": {
"model": "@cf/meta/llama-3.3-70b-instruct-fp8-fast",
"source": "chat model (Workers AI, Groq fallback), JSON output",
"results": [
{
"index": 0,
"labels": [
{
"label": "complaint",
"confidence": 0.95
}
],
"reason": "Reports damage and no support reply.",
"excerpt": "The package arrived broken and support never answered."
},
{
"index": 1,
"labels": [
{
"label": "praise",
"confidence": 0.93
}
],
"reason": "Thanks the team for a fast fix.",
"excerpt": "Great service, the team fixed it in an hour."
}
],
"checkedAt": "2026-10-14T07:05:00.000Z"
}
}Tags
x402
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