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x402 Text-to-Structure Extraction API
x402
x402 Text-to-Structure Extraction API is a paid API for AI agents from call402.com, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-19).
Extracts structured entities and key-value pairs from free-form text, including dates, emails, money amounts, URLs, EVM addresses, and custom fields
Price / call$0.005
Success—
StatusUnknown
How this service works
USDC pay-per-call APIs for AI agents via the x402 protocol (v2, exact, Base mainnet). Send a request, receive HTTP 402 with payment requirements, pay with @x402/fetch or any x402 client, retry with the PAYMENT-SIGNATURE header.
Output
A JSON object containing an 'entities' map with arrays for recognized entity types (dates with ISO format, money with amount and currency, emails, URLs, phone numbers, EVM addresses, tx_hashes, percentages, key_values), plus a 'structured' object with values mapped to any requested schema fields or named fields, and a 'meta' object with character count and extractor version.
Example use cases
Invoice data extraction for accounting
“I have this invoice email body — can you extract the amount due, due date, and vendor email address from it as structured fields? Here's the text: 'Your invoice for $1,200.00 is due on 2025-08-15. Please contact billing@vendor.com with questions.'”
Crypto transaction log parsing
“Parse this transaction log and pull out any Ethereum wallet addresses, transaction hashes, and USD amounts: '0xAbC...sent 0.5 ETH ($1,500.00) in tx 0x1a2b...'”
Custom field extraction from user notes
“From this freeform note, extract the fields 'client_name', 'deadline', and 'budget': 'Need to deliver the redesign for Acme Corp by March 3rd 2026, budget is capped at $8,000.'”
Technical details
Endpoint
POSThttps://call402.com/api/text-to-structure?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to extract structured entities or named fields from unstructured text without building a custom NLP pipeline. It is especially useful for AI agents that receive natural language inputs and need to feed downstream systems with typed data. The deterministic extraction engine is fast and cheap ($0.01 USDC) for high-frequency, low-latency extraction tasks. Prefer it over LLM-based extraction when you want predictable, schema-defined outputs and cost control.
Side effects
- Text exceeds 20,000 character limit — request rejected
- Invalid JSON schema provided in the 'schema' field — parsing error returned
- No entities found matching requested fields — structured object returned empty
- x402 payment not attached or invalid PAYMENT-SIGNATURE header — HTTP 402 returned with payment requirements
- Malformed request body — HTTP 400 with validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | Input text, max 20000 chars |
| fields | array | Optional field names to extract |
| schema | object | Optional JSON-schema-like object with properties to fill |
Output
Schema · Response
{
"type": "json",
"example": {
"meta": {
"chars": 108,
"extractor": "deterministic-v1"
},
"entities": {
"urls": [],
"dates": [
{
"iso": "2026-11-01",
"raw": "2026-11-01"
}
],
"money": [
{
"raw": "$250.00",
"amount": 250,
"currency": "USD"
}
],
"emails": [
"alice@example.com"
],
"phones": [],
"tx_hashes": [],
"key_values": {},
"percentages": [
{
"raw": "2.5%",
"value": 2.5
}
],
"evm_addresses": []
},
"structured": {
"date": "2026-11-01",
"email": "alice@example.com",
"amount": 250
}
}
}Tags
x402
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