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Relaystation Data Validate
x402
Relaystation Data Validate is a paid API for AI agents from api.relaystation.ai, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-14).
Validates rows in a CSV or structured file against a user-defined JSON schema, returning row-level validation errors as JSON
Price / call$0.01
Success—
StatusUnknown
How this service works
$0.0002/MB. Validate rows against a structured schema — row-level errors back as JSON. 1¢ x402 min; remainder auto-credits — relaystation.ai/penny
Output
Returns a JSON object with a 'valid' boolean, an 'errors' array listing row-level validation failures with field details, a 'rowCount' integer, an 'errorCount' integer, and a 'truncated' boolean indicating whether the result set was cut off.
Example use cases
Pre-import data quality check
“Before I import this CSV of customer records into our database, can you validate it against our schema where name and email are required strings and age must be an integer between 0 and 120? Flag any rows that don't pass.”
Pipeline bad-row detection
“I'm running a data pipeline and need to catch any rows in this CSV that violate our structure — product_id is a required integer, price is a required number with min 0, and category is a required string. Can you check all rows and return the errors as JSON?”
User-uploaded file validation
“A user just uploaded a CSV form submission. Can you check every row against my schema — first_name and last_name are required strings, zip_code is a required string, and score is an integer between 1 and 100 — and tell me how many rows are invalid and why?”
Technical details
Endpoint
POSThttps://api.relaystation.ai/v1/data/validateWhen to prefer
Choose this endpoint when you need to validate structured tabular data (CSV) row-by-row against a typed schema with constraints like required fields, type checking, and numeric min/max bounds. It is purpose-built for pre-import quality gates, ETL pipeline validation, and user file submission checks — especially when you need row-level error detail rather than a simple pass/fail, and when you want a pay-per-call model with no infrastructure to manage.
Side effects
- Malformed base64 input causes parsing failure
- Schema fields with unsupported types are rejected
- CSV missing headers causes column-mapping errors
- File too large may result in truncated results (truncated: true)
- Invalid JSON schema structure returns schema parse error
- Missing required body fields (file, schema, from) causes 400-level error
Input
{
"input": {
"body": {
"file": {
"inline": "bmFtZSxhZ2UsY2l0eQpKb2huLDI4LFNhbiBGcmFuY2lzY28KSmFuZSwyNSxOZXcgWW9yawpCb2IsNDUsQm9zdG9u"
},
"from": "csv",
"schema": {
"age": {
"max": 150,
"min": 0,
"type": "integer",
"required": true
},
"city": {
"type": "string",
"required": true
},
"name": {
"type": "string",
"required": true
}
}
},
"type": "http",
"method": "POST",
"bodyType": "json"
},
"output": {
"type": "object",
"example": {
"valid": true,
"errors": []
}
}
}Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
{
"valid": true,
"errors": [],
"rowCount": 3,
"truncated": false,
"errorCount": 0
}Tags
x402
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