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Data Quality Gate
x402
Data Quality Gate is a paid API for AI agents from www.aidatatools.dev, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-02).
Analyzes a JSON dataset and returns a deterministic quality verdict (RELIABLE / USABLE_WITH_CLEANING / UNRELIABLE) with metrics on completeness, nulls, type consistency, duplicates, outliers, and cardinality.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data Quality Gate: deterministic dataset quality verdict
Output
Returns a deterministic quality verdict — one of RELIABLE, USABLE_WITH_CLEANING, or UNRELIABLE — along with exact metrics: completeness percentage, null counts per field, type consistency flags, impossible value detections, duplicate row counts, outlier identifications, and cardinality statistics per column. Results are fully reproducible: identical input always produces identical output.
Example use cases
Pre-pipeline data ingestion audit
“Before we load this JSON dataset into the warehouse, can you run a quality gate on it and tell me if it's reliable or needs cleaning? Flag any nulls, duplicates, or impossible values you find.”
ML training data readiness check
“I'm about to train a model on this JSON dataset — can you check its data quality and give me a verdict on whether it's reliable enough, along with completeness and outlier stats?”
Third-party data vendor validation
“We just received a JSON data export from a vendor. Can you run it through a quality check and tell me if the data is reliable, what the completeness looks like, and whether there are any type inconsistencies or duplicates?”
Technical details
Endpoint
POSThttps://www.aidatatools.dev/api?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, deterministic, traceable data quality verdict on a JSON dataset with no probabilistic or LLM-based variability — ideal for CI/CD data pipelines, pre-model training checks, and vendor data audits where reproducibility and exact metrics matter. Prefer it over LLM-based data review when you need identical outputs for identical inputs and auditable fact-based metrics rather than narrative summaries.
Side effects
- Invalid or malformed JSON payload returns a parsing error
- Empty dataset or zero-row input may return an error or trivial verdict
- Extremely large datasets may time out or exceed payload size limits
- Missing required fields in the JSON body result in a 400-level error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"score": 83.75,
"checks": [
{
"name": "text_cleanliness",
"detail": "2 of 30 values carry mojibake",
"status": "warn"
}
],
"verdict": "RELIABLE"
}
}Tags
x402
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Zero review prompt (fires after every successful call)
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