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Strale Data Quality Check
x402
Strale Data Quality Check is a paid API for AI agents from api.strale.io, paid per call via x402, $0.0216/call, status unknown (last checked 2026-09-15).
Validates a JSON array of objects against custom rules (email_format, range, pattern, etc.) or auto-detects issues, returning a quality score and field-level report
Price / call$0.0216
Success—
StatusUnknown
How this service works
Check data quality with custom rules (email_format, range, pattern, etc.) or auto-detect issues. Returns quality score and field-level report. Algorithmic.
Output
Returns an overall quality score (0–100) and a field-level report identifying which records and fields failed which rules, including auto-detected anomalies if no rules are specified.
Example use cases
Verify customer records before CRM import
“I'm about to load 500 customer records into our CRM system. Can you validate them for me? Check that all emails are properly formatted, phone numbers match a pattern like +1-XXX-XXX-XXXX, and that any revenue fields are positive numbers. Let me know which records have problems so I can clean them up first.”
Audit financial transaction dataset quality
“We just received a batch of transaction data from our accounting team. I need you to validate it before we process payroll. Make sure all amounts are between 0 and 999999, dates follow YYYY-MM-DD format, and employee IDs are 6-digit numbers. Give me a quality score and tell me exactly which rows are broken.”
Check for malformed product data
“Our e-commerce platform is acting up and I suspect some product records got corrupted in the database. Can you scan this JSON export and auto-detect any data quality issues? I don't have specific rules — just find missing fields, weird values, and anything else that looks wrong. Then give me a summary so I know what to fix.”
Technical details
Endpoint
GEThttps://api.strale.io/x402/data-quality-checkWhen to prefer
Use this endpoint when you need programmatic, rule-based data validation with a structured quality score and field-level breakdown — especially when you have JSON records to check for format compliance, range violations, or pattern mismatches before ingesting into a pipeline or database.
Side effects
- Missing required 'data' query parameter returns error
- Malformed JSON array causes parse error
- Invalid rule type (e.g. unsupported rule name) may be ignored or return error
- Empty data array returns score of 100 with no issues
- Very large arrays may time out or be truncated
Input
{
"data": [
{
"age": 28,
"name": "Alice",
"email": "alice@example.com"
},
{
"age": 35,
"name": "Bob",
"email": "bob@example.com"
},
{
"age": 42,
"name": "Charlie",
"email": "invalid-email"
}
],
"rules": [
{
"type": "email_format",
"field": "email"
},
{
"max": 120,
"min": 0,
"type": "range",
"field": "age"
}
]
}Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object |
Output
{
"_meta": {
"payment": {
"method": "x402",
"price_usd": 0.0216,
"settlement_id": "0x90f025c168d3adc6db9ef3956e7c60ad1edda139eb3f6147ca93b749dc62934e"
},
"capability": "data-quality-check",
"latency_ms": 0,
"provenance": {
"source": "algorithmic",
"fetched_at": "2026-06-13T04:23:21.859Z"
}
},
"issues": [],
"total_rows": 3,
"valid_rows": 3,
"issue_count": 0,
"field_report": [
{
"field": "name",
"null_count": 0,
"completeness": 100,
"unique_count": 3
},
{
"field": "email",
"null_count": 0,
"completeness": 100,
"unique_count": 3
},
{
"field": "age",
"null_count": 0,
"completeness": 100,
"unique_count": 3
}
],
"invalid_rows": 0,
"quality_score": 100
}Tags
x402
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