# WhatChuNeed LLM Extract — Structured Data Extraction from Text

> WhatChuNeed LLM Extract — Structured Data Extraction from Text is a paid API for AI agents from whatchuneed.vercel.app, paid per call via x402, $0.1/call, status unknown (last checked 2026-09-14).

Extracts structured data from unstructured text according to a caller-specified JSON schema, returning typed fields with confidence scores

## Facts

- Endpoint: POST https://whatchuneed.vercel.app/api/llm/extract
- Price: $0.1/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-14
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/whatchuneed-llm-extract-structured-data-extraction-from-text-3568f1e3
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_wBGwOg3pg0cwRkhQgReXO

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 whatchuneed-llm-extract-structured-data-extraction-from-text-3568f1e3 -d '<json body>'
```

Example prompt: Extract the patient name, date of birth, diagnosis, and prescribed medications from this clinical note and return them as structured JSON: 'John Doe, DOB 1978-04-12, presented with hypertension, prescribed lisinopril 10mg and aspirin 81mg.'

## When to prefer this

Choose this endpoint when you have raw, unstructured text and need to map it to a specific typed schema — especially when field names and types are known in advance. It is ideal over general-purpose LLM chat endpoints when you need deterministic JSON output with a confidence signal and explicit field attribution. Prefer it over regex or rule-based parsers when the text format is variable or semi-structured.

## Known failure modes

- Schema mismatch: if the schema specifies fields not present in the text, those fields may be absent or null in the output
- Low confidence: ambiguous or incomplete text may yield a confidence score near 0 indicating unreliable extraction
- Malformed schema: if the schema object is invalid JSON or empty, the endpoint may return a 400 error
- Text too short or vague: very sparse input may result in an empty data object with zero source_fields
- Rate or payment issues: without valid x402 payment, the endpoint returns 402 Payment Required

## How this service works

320+ pay-per-call API endpoints across accommodation, LLM, code execution, crypto, medical, finance, and more. One API, one payment. Powered by x402 protocol.

## Output

Returns a JSON object with three fields: 'data' containing the extracted values mapped to the caller-specified schema keys, 'confidence' as a 0–1 float indicating how reliably the extraction was performed, and 'source_fields' as an array of field names that were successfully populated from the source text.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "required": [
  "text",
  "schema"
 ],
 "properties": {
  "text": {
   "type": "string"
  },
  "schema": {
   "type": "object",
   "description": "Expected output structure"
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "data": {
   "type": "object"
  },
  "confidence": {
   "type": "number"
  },
  "source_fields": {
   "type": "array",
   "items": {
    "type": "string"
   }
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/whatchuneed-llm-extract-structured-data-extraction-from-text-3568f1e3/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from whatchuneed.vercel.app](https://www.zero.xyz/host/whatchuneed.vercel.app/llms.txt)