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X402 Cloud JSON Structurer
x402
X402 Cloud JSON Structurer is a paid API for AI agents from api.x402cloud.space, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-13).
Transforms arbitrary input objects into structured JSON output using AI-powered schema inference and data normalization
Price / call$0.01
Success—
StatusUnknown
How this service works
Agent-ready x402 APIs for AI video production, audio localization, web research, document extraction, data analysis, and structured JSON transformation.
Output
Returns a JSON object containing a 'result' field with the AI-structured output, a 'status' string indicating success or failure, a 'request_id' for tracking, optionally 'model_used' indicating which AI model performed the transformation, 'usage' stats, 'payment' details, and any 'warnings' about the transformation.
Example use cases
Normalize e-commerce order payload
“I've got this raw order object coming from our legacy system with inconsistent field names and mixed types — can you restructure it into a clean normalized JSON with proper field names and consistent data types? Here's the object: {cust_nm: 'Acme Corp', ord_no: '9981', ttl: '199.99', itms: [{nm: 'Widget A', qty: '3'}]}.”
Structure scraped web data
“I scraped some product data from a website and it came back as a messy nested object — can you turn it into a clean, consistently structured JSON with fields like productName, price, description, and availability? Here's what I got: {prod: 'Blue Widget', $price: '$24.99', 'in-stock': 'yes', desc: 'A small blue widget'}.”
Reformat API response for database ingestion
“I'm getting back a weird JSON payload from a third-party API that I need to clean up before inserting into our database — can you restructure it into a flat JSON with snake_case field names? The input is: {UserID: 445, 'Full-Name': 'Bob Smith', emailAddr: 'bob@example.com', acct_status: 1}.”
Technical details
Endpoint
POSThttps://api.x402cloud.space/api/v1/json-structurerWhen to prefer
Choose this endpoint when you need to normalize, reformat, or structure arbitrary JSON objects into a consistent schema using AI inference. It is ideal for cleaning up heterogeneous data from legacy systems, web scraping outputs, or third-party APIs before downstream processing. Prefer this over manual ETL scripts when the input schema varies unpredictably or when field names and types are inconsistent.
Side effects
- Missing or empty 'input' field returns validation error
- Malformed JSON in request body causes 400 error
- Payment failure (insufficient USDC balance) blocks request processing
- Ambiguous or empty input object may result in pass-through with warnings
- Rate limiting may occur under high request volume
- Model unavailability may cause 503 or timeout responses
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| options | object |
Output
Schema · Response
| Field | Type | Description |
|---|---|---|
| usage | object | |
| resultrequired | object | |
| statusrequired | string | |
| payment | object | |
| warnings | array | |
| model_used | string | |
| request_idrequired | string |
Tags
x402
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