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dataforge — JSON Schema Inference API
x402
dataforge — JSON Schema Inference API is a paid API for AI agents from dataforge.x.c00l.site, paid per call via x402, $0.003/call, status unknown (last checked 2026-09-14).
Infers a JSON Schema from a provided data payload (CSV, JSON, YAML, TOML, XML, etc.), returning a draft 2020-12 schema describing the data's structure and types.
Price / call$0.003
Success—
StatusUnknown
How this service works
Deterministic conversion, JSONPath query, JSON Schema inference, declarative transforms and RFC 6902 diff/patch across CSV, TSV, JSON, JSONL, YAML, TOML, XML, INI and properties. Pay per call with x402 (USDC on Base).
Output
Returns a JSON Schema object (draft 2020-12) describing the inferred structure of the input data, including property names, detected types (integer, string, null, etc.), format annotations (e.g. email), and which fields appear required based on the sample data.
Example use cases
Auto-generate schema for API validation
“I have some sample JSON records with fields like user_id, email, and subscription_plan — can you infer a JSON Schema draft 2020-12 so I can use it to validate future API payloads?”
Discover structure of unfamiliar CSV data
“I got this CSV file from a vendor and I have no documentation — can you infer its JSON Schema so I can understand what fields and types it contains?”
Generate schema from YAML config
“Here's a YAML config file from our infrastructure team — can you run schema inference on it and give me back a JSON Schema so I can document the expected structure?”
Technical details
Endpoint
POSThttps://dataforge.x.c00l.site/schemaWhen to prefer
Choose this endpoint when you need deterministic, format-agnostic JSON Schema inference from raw structured data (CSV, JSON, YAML, TOML, XML, etc.) without standing up your own parsing infrastructure. It is especially useful when you need draft 2020-12 compliant output and want to pay per call rather than maintain a schema inference service. Prefer it over general-purpose LLM schema guessing when you need reliable, machine-verifiable output.
Side effects
- Malformed or unparseable input data returns an error
- Unsupported format type causes rejection
- Ambiguous or empty input may produce an overly permissive schema
- Very large payloads may be truncated or rejected
- Missing mode parameter may default unexpectedly
Output
Schema · Response
{
"type": "json",
"example": {
"mode": "infer",
"schema": {
"type": "array",
"items": {
"type": "object",
"required": [
"id",
"email",
"tier"
],
"properties": {
"id": {
"type": "integer"
},
"note": {
"type": "null"
},
"tier": {
"type": "string"
},
"email": {
"type": "string",
"format": "email"
}
}
},
"$schema": "https://json-schema.org/draft/2020-12/schema"
}
}
}Tags
x402
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