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uxus.finance Schema Extraction
x402
uxus.finance Schema Extraction is a paid API for AI agents from uxus.finance, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-14).
Extracts structured data from text or a URL into a caller-defined JSON schema, returning the populated object and the model used.
Price / call$0.01
Success—
StatusUnknown
How this service works
Pull structured JSON out of messy text or a web page — no API key, no account, no signup; pay per call in USDC on Base. POST JSON { schema: { field: type, ... } (required), and text or url }. If url is given the page is fetched and stripped first; an LLM fills the schema and missing fields come back null. Returns { data, model, latency_ms }. For agents turning an article, listing, or product page into typed fields.
Output
A JSON object populated with values extracted from the input text or URL, keyed to the schema fields the caller defined in the POST body, plus a 'model' field identifying which AI model performed the extraction.
Example use cases
Structured article metadata extraction
“Parse this news article URL https://techcrunch.com/2024/01/ai-funding and extract it into this schema: {"headline": "", "author": "", "publishDate": "", "summary": "", "tags": []} — fill in every field you can find on the page.”
Resume parsing into standard fields
“Here's a raw resume text: 'Jane Doe, 5 years Python, worked at Google 2019-2023...' — extract it into this schema: {"name": "", "skills": [], "experience": [{"company": "", "years": 0}]} and give me the populated JSON.”
E-commerce product data structuring
“Pull structured data from this Amazon listing https://amazon.com/dp/B09XYZ and map it to my schema: {"title": "", "price": 0, "rating": 0, "reviewCount": 0, "bulletPoints": []} so I can store it in my database.”
Technical details
Endpoint
POSThttps://uxus.finance/api/extractWhen to prefer
Choose this endpoint when you need to convert unstructured text or a web page into a caller-defined JSON schema in a single API call. It is especially useful when you control the output schema shape and want extraction plus model attribution in one response. Prefer it over general LLM completions when you need strict schema conformance, and over generic scrapers when you need semantic field mapping rather than raw HTML.
Side effects
- URL is bot-blocked or returns a non-200 response — extraction fails or returns partial data
- Schema fields cannot be inferred from the content — fields returned as null or empty
- Malformed JSON schema in the POST body — validation error returned
- Text or URL content is too short or ambiguous to fill the schema — low-confidence or empty values
- Network timeout fetching the URL — request fails with timeout error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| url | string | Page to fetch and extract from. Provide this or text. |
| text | string | Raw source text. Provide this or url. |
| schema | object | Fields you want, e.g. { title: string, price: number }. Required. |
Output
Schema · Response
{
"type": "json",
"example": {
"data": {
"price": 19.99,
"title": "Example",
"in_stock": true
},
"model": "deepseek/deepseek-chat",
"latency_ms": 900
}
}Tags
x402
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