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JSON Schema Inference API
x402
JSON Schema Inference API is a paid API for AI agents from api.24klabs.ai, paid per call via x402, $0.004/call, status unknown (last checked 2026-09-15).
Infers a JSON Schema (draft 2020-12) from one or more sample JSON documents, detecting types, nested structures, arrays, and required fields.
Price / call$0.004
Success—
StatusUnknown
How this service works
Infer a JSON Schema (draft 2020-12) from one or more sample documents: type unions, nested objects/arrays, and required-field detection.
Output
Returns a JSON Schema (draft 2020-12) object derived from the provided sample documents, including inferred property types (with type unions where values vary), identification of required vs optional fields based on presence across samples, and full handling of nested objects and arrays.
Example use cases
API contract generation from examples
“I have three sample API response payloads and I want you to infer a JSON Schema draft 2020-12 from them so I can use it for validation — here they are: [{"userId":1,"email":"a@b.com","active":true},{"userId":2,"email":"c@d.com","active":false},{"userId":3,"email":"e@f.com"}]”
Detecting required vs optional fields
“Can you analyze these JSON samples and tell me which fields are always present (required) vs sometimes missing, and generate a JSON Schema for it? Here's the data: [{"name":"Widget","price":9.99,"sku":"W1"},{"name":"Gadget","price":19.99},{"name":"Doohickey","price":4.99,"sku":"D3"}]”
Schema inference for nested document structure
“I've got some nested JSON documents with arrays and sub-objects and I need a JSON Schema draft 2020-12 inferred from them — including the nested types: [{"order":{"id":100,"items":[{"product":"Apple","qty":2}]},"status":"shipped"},{"order":{"id":101,"items":[{"product":"Banana","qty":1},{"product":"Cherry","qty":5}]},"status":"pending"}]”
Technical details
Endpoint
POSThttps://api.24klabs.ai/api/v1/json-schema-inferWhen to prefer
Choose this endpoint when you need to automatically derive a JSON Schema draft 2020-12 from real example data without writing the schema by hand, especially when you have multiple samples and want accurate required-field detection and type-union inference. Prefer it over manual schema authoring or generic LLM prompting when precision and draft-2020-12 compliance matter.
Side effects
- Invalid or malformed JSON input returns a parsing error
- Empty input array returns an error or minimal schema
- Highly inconsistent samples may produce overly permissive schemas with many type unions
- Very deeply nested structures may hit recursion limits
- Single sample may miss optional fields, producing overly strict required lists
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| samples | array |
Output
Schema · Response
{
"type": "json",
"example": {
"schema": {
"type": "object",
"required": [
"id",
"name"
]
}
}
}Tags
x402
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