# netintel.dev Schema Parse Extract

> netintel.dev Schema Parse Extract is a paid API for AI agents from netintel.dev, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-14).

Extracts structured contact and entity fields (name, role, email, phone, company) from unstructured text using LLM-based parsing

## Facts

- Endpoint: POST https://netintel.dev/schema-parse/extract
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-14
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/netintel-dev-schema-parse-extract-ecd7fd98
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_0aHUoQg2XjXIMjANL6p_X

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 netintel-dev-schema-parse-extract-ecd7fd98 -d '<json body>'
```

Example prompt: Extract the structured contact info from this text — name, role, email, phone, and company: 'Reach out to Sarah Chen, VP of Engineering at Acme Corp, at sarah.chen@acme.com or 555-867-5309.'

## When to prefer this

Use this endpoint when you need to extract structured contact or entity fields (name, role, email, phone, company) from unstructured text such as email signatures, bios, scraped profiles, or document snippets. It is pay-per-call at $0.10 USDC via x402 micropayments, making it suitable for on-demand extraction without subscription commitments. Prefer this over regex-based parsers when input format is varied or freeform.

## Known failure modes

- Input text contains no recognizable contact entities — returns empty or partial extracted object
- Ambiguous text with multiple people mentioned — may return only the most prominent entity
- Malformed or truncated input — may return incomplete fields
- Payment failure via x402 — 402 response with payment requirements
- Rate limiting or service unavailability — 429 or 5xx response

## How this service works

Extract structured data from any unstructured text into your own JSON Schema — structured-data / information extraction, text-to-JSON, LLM data enrichment. You supply the schema; the LLM returns a matching object. Works for contacts, invoices, events, product specs, medical records, legal clauses, resumes — any shape. Returns the extracted object plus token usage; unfound fields are omitted or null.

## Output

Returns a JSON object with extracted fields including name, role, email, phone, and company pulled from the input text, plus token usage counts for input and output tokens consumed.

## Request schema (JSON Schema)

```json
{
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "type": "object",
 "properties": {
  "input": {
   "type": "object",
   "properties": {
    "type": {
     "type": "string",
     "const": "http"
    },
    "method": {
     "type": "string",
     "enum": [
      "POST"
     ]
    },
    "bodyType": {
     "type": "string",
     "enum": [
      "json",
      "form-data",
      "text"
     ]
    },
    "body": {
     "type": "object",
     "properties": {
      "raw_text": {
       "type": "string",
       "description": "Unstructured text to extract data from — emails, resumes, articles, log entries, contracts, support tickets, etc."
      },
      "target_schema": {
       "type": "object",
       "description": "A standard JSON Schema object with type, properties, and optionally required. Defines the shape of the extracted output."
      }
     },
     "required": [
      "raw_text",
      "target_schema"
     ]
    }
   },
   "required": [
    "type",
    "method",
    "bodyType",
    "body"
   ],
   "additionalProperties": false
  },
  "output": {
   "type": "object",
   "properties": {
    "type": {
     "type": "string"
    },
    "example": {
     "type": "object",
     "properties": {
      "extracted": {
       "type": "object",
       "description": "Structured data conforming to the caller's target_schema. Missing fields are omitted or null."
      },
      "tokens_used": {
       "type": "object",
       "description": "LLM token consumption for this extraction",
       "properties": {
        "input": {
         "type": "number",
         "description": "Prompt tokens consumed"
        },
        "output": {
         "type": "number",
         "description": "Completion tokens consumed"
        }
       }
      }
     }
    }
   },
   "required": [
    "type"
   ]
  }
 },
 "required": [
  "input"
 ]
}
```

## Response schema (JSON Schema)

```json
{
 "type": "json",
 "example": {
  "extracted": {
   "name": "Sarah Chen",
   "role": "VP of Engineering",
   "email": "sarah.chen@acme.com",
   "phone": "555-867-5309",
   "company": "Acme Corp"
  },
  "tokens_used": {
   "input": 312,
   "output": 48
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/netintel-dev-schema-parse-extract-ecd7fd98/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from netintel.dev](https://www.zero.xyz/host/netintel.dev/llms.txt)