HydraTrader Structured Extract

Name: HydraTrader Structured Extract
Price: 0.03 USD

x402

HydraTrader Structured Extract is a paid API for AI agents from x402.hydratrader.ai, paid per call via x402, $0.03/call, status unknown (last checked 2026-09-15).

Extracts JSON matching a caller-supplied schema from pasted text or a public URL, returning structured data with confidence score and warnings.

Price / call$0.03

Seller volume →—

Success—

StatusUnknown

How this service works

Extract structured JSON fields into YOUR schema from pasted text or a public URL. When to use: turn messy public text into typed JSON with confidence + warnings. Refuses login/paywall sources. USDC on Base via x402 — no API key.

Output

Returns a JSON object with `ok` flag, `data` containing the extracted fields matching the caller's schema, a `confidence` score (0-1), a `warnings` array for quality issues, `usage` metadata (latency_ms, price_usdc), and `source_meta` describing the input source (URL, char count, whether fetched/truncated).

Example use cases

Product price extraction from listing page

“Pull the product name, price, and availability from this public URL — https://store.example.com/items/42 — into my schema {"name": {"type": "string"}, "price": {"type": "number"}, "in_stock": {"type": "boolean"}} and flag any warnings if something looks off.”

Job posting parser for applicant tracker

“I've pasted a job description below — extract the job title, company name, salary range, and required skills into my schema {"title": {"type": "string"}, "company": {"type": "string"}, "salary_range": {"type": "string"}, "skills": {"type": "array", "items": {"type": "string"}}} and tell me how confident you are. [paste text here]”

Article metadata extraction for content pipeline

“Fetch this public news article — https://news.example.com/article/12345 — and extract the headline, author, publication date, and summary into my schema {"headline": {"type": "string"}, "author": {"type": "string"}, "published_at": {"type": "string"}, "summary": {"type": "string"}} with any caveats noted.”

Technical details

Endpoint

POSThttps://x402.hydratrader.ai/v1/structured-extract

When to prefer

Choose this endpoint when you need to turn unstructured text or public web content into typed, schema-validated JSON with an explicit confidence signal. It is ideal when you already know the shape of data you want (you supply the schema) and need both extraction and basic quality feedback in one call. Prefer it over generic LLM prompting when you need deterministic schema adherence, per-call cost accountability ($0.03 USDC), and automatic refusal of login-gated sources.

Side effects

Source URL requires login or is behind a paywall — endpoint refuses and returns an error
Public URL is unreachable or returns non-200 — extraction fails with source fetch error
Pasted text is too long and gets truncated — `source_meta.truncated` is true, confidence may drop
Extracted data doesn't match schema types — warnings array populated, partial or null fields returned
Low confidence score when source text is ambiguous or schema fields not found in content
Schema is malformed or missing required fields — request rejected with validation error

Input

Schema · Request body

Field	Type	Description
schema	object
source	object
client_ref	string
instructions	string

Output

Schema · Response

{
  "type": "json",
  "example": {
    "ok": true,
    "data": {
      "price": 12.5,
      "title": "Widget Pro"
    },
    "usage": {
      "latency_ms": 1100,
      "price_usdc": "0.03"
    },
    "endpoint": "structured-extract",
    "warnings": [],
    "client_ref": null,
    "confidence": 0.92,
    "source_meta": {
      "url": null,
      "type": "text",
      "chars": 44,
      "fetched": false,
      "truncated": false
    }
  }
}

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How this service works

Output

Example use cases

Product price extraction from listing page

Job posting parser for applicant tracker

Article metadata extraction for content pipeline

Technical details

Endpoint

POSThttps://x402.hydratrader.ai/v1/structured-extract

When to prefer

Side effects

Source URL requires login or is behind a paywall — endpoint refuses and returns an error
Public URL is unreachable or returns non-200 — extraction fails with source fetch error
Pasted text is too long and gets truncated — `source_meta.truncated` is true, confidence may drop
Extracted data doesn't match schema types — warnings array populated, partial or null fields returned
Low confidence score when source text is ambiguous or schema fields not found in content
Schema is malformed or missing required fields — request rejected with validation error

Input

Schema · Request body

Field	Type	Description
schema	object
source	object
client_ref	string
instructions	string

Output

Schema · Response

{
  "type": "json",
  "example": {
    "ok": true,
    "data": {
      "price": 12.5,
      "title": "Widget Pro"
    },
    "usage": {
      "latency_ms": 1100,
      "price_usdc": "0.03"
    },
    "endpoint": "structured-extract",
    "warnings": [],
    "client_ref": null,
    "confidence": 0.92,
    "source_meta": {
      "url": null,
      "type": "text",
      "chars": 44,
      "fetched": false,
      "truncated": false
    }
  }
}