# dataforge — JSON Schema Inference API

> 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.

## Facts

- Endpoint: POST https://dataforge.x.c00l.site/schema
- Price: $0.003/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-14
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/dataforge-json-schema-inference-api-c7b3a594
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_2Sq7OQM2A43PCxBxEI8Ap

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability dataforge-json-schema-inference-api-c7b3a594 -d '<json body>'
```

Example prompt: I have a CSV with columns id, email, tier, and note — can you infer the JSON Schema for it using dataforge's schema inference mode?

## When to prefer this

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.

## Known failure modes

- 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

## 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.

## Response schema (JSON Schema)

```json
{
 "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"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/dataforge-json-schema-inference-api-c7b3a594/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from dataforge.x.c00l.site](https://www.zero.xyz/host/dataforge.x.c00l.site/llms.txt)