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netintel.dev Schema Validator
x402
netintel.dev Schema Validator is a paid API for AI agents from netintel.dev, paid per call via x402, $0.02/call, status unknown (last checked 2026-09-15).
Validates JSON data against a supplied schema, returning precise errors with JSON paths, with optional type coercion and repair of invalid data.
Price / call$0.02
Success—
StatusUnknown
How this service works
Validate data against a supplied schema — checks required fields, nested objects, arrays, enums, string formats, numeric ranges, lengths, and additional-property rules deterministically, with optional type coercion and repair, returning precise errors with JSON paths so agents can verify structured data before acting on it.
Output
Returns a JSON object with: valid (boolean), error_count, errors array (each with JSON path and description), grade (letter), score (0-1), coerced (boolean indicating if coercion was applied), repaired (fixed copy of data if repair=true), is_repaired_valid (boolean or null), warnings, and findings.
Example use cases
Pre-save form data validation
“Before I write this user registration payload to the database, validate it against my schema: required fields are name, email, and age as a number. Coerce types if possible and tell me exactly which fields are wrong with their JSON paths.”
API response conformance check
“I just got a response back from an external API — can you validate it against this expected schema with required fields id (uuid), status (enum: active/inactive), and created_at (date-time), and flag any violations?”
Repair malformed configuration object
“This config JSON I received is probably malformed — validate it against my schema for required keys host (hostname), port (number), and debug (boolean), and if it fails, give me a repaired version with coercion enabled so I can see what it should look like.”
Technical details
Endpoint
POSThttps://netintel.dev/schema/validateWhen to prefer
Choose this endpoint when you need deterministic, schema-driven validation with precise JSON-path error reporting before acting on structured data. Prefer it over LLM-based checks when you need repeatable, auditable results. The optional coercion and repair features make it especially useful in pipelines where upstream data is loosely typed or partially malformed. It supports both full JSON Schema and a simplified shorthand map, making it accessible for quick field checks without writing verbose schemas.
Side effects
- Data exceeds 64KB limit — returns error
- Malformed input schema that cannot be parsed — returns schema parse error
- Invalid bodyType for the payload — returns 400-level error
- LLM repair fallback disabled (allow_llm=false) and deterministic repair insufficient — repaired copy may still be invalid
- Ambiguous simplified field-type map syntax — may produce unexpected validation behavior
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"grade": "A",
"score": 100,
"valid": false,
"errors": [
{
"path": "$.email",
"message": "required field is missing",
"expected": "required",
"received": "undefined"
},
{
"path": "$.age",
"message": "expected number, received string",
"expected": "number",
"received": "string"
}
],
"coerced": false,
"findings": [
{
"rule": "data_invalid",
"detail": "Data does not satisfy the schema (2 errors)",
"deduction": 0
}
],
"repaired": null,
"warnings": [],
"error_count": 2,
"is_repaired_valid": null
}
}Tags
x402
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