Machine Shop – Webpage Document Preparer

x402

Machine Shop – Webpage Document Preparer is a paid API for AI agents from api.pay-forge.online, paid per call via x402, $0.015/call, status unknown (last checked 2026-10-20).

Fetches a URL and converts it into chunked, entity-tagged document form ready for evidence extraction, field comparison, and downstream analysis.

Price / call$0.015

Seller volume →—

Success—

StatusUnknown

How this service works

Turn Apify product scrape results into safe catalog updates with Catalog Update Gate. Find evidence in supplied documents, compare snapshots, extract typed JSON fields, inspect x402 quotes and assess API migrations. Preview inputs, evidence and cost for free. No live web search, scheduled monitoring or third-party purchasing.

Output

Returns the full page text, markdown rendering, metadata (title, language, description, canonical URL), a list of text chunks with start/end positions and index, a SHA-256 hash of the document, extracted entities (URLs, dates, money values, emails, EVM addresses), any structured data found, and a list of parsing limitations (e.g. no JavaScript rendering).

Example use cases

Extract contract terms from URL

“Pull down this contract page at https://acme.com/legal/vendor-agreement and break it into chunks of 1000 characters with 200 overlap so I can search for payment terms, dates, and any dollar amounts mentioned.”

Snapshot webpage for comparison

“Fetch https://competitor.com/pricing and prepare the document — I want the SHA-256 hash, all the extracted text, and any money entities so I can compare it against last week's snapshot.”

Parse API docs for field extraction

“Grab the page at https://docs.someapi.com/endpoints and convert it into a chunked document with default chunk size so I can extract all the endpoint URLs and email addresses listed on it.”

Technical details

Endpoint

POSThttps://api.pay-forge.online/v1/webpage-prepare-document?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need to prepare a static webpage as a structured, chunked document for downstream evidence extraction, entity parsing, field comparison, or LLM ingestion. It is ideal when you need reproducible content (via SHA-256 hash) for snapshot diffing, or when you need named entities like dates, money amounts, emails, and EVM addresses automatically extracted alongside the text. Prefer alternatives if you need JavaScript-rendered pages, live web search, scheduled monitoring, or table/microdata parsing.

Side effects

URL is unreachable or returns non-200 status — fetch fails with an error
Page relies on JavaScript rendering — static HTML only, dynamic content will be missing
chunk_size below 64 or above 10000 — rejected by schema validation
URL exceeds 2048 characters — rejected by schema validation
Page has no meaningful text content — returns empty or near-empty chunks
Rate limiting or payment failure — 402 response requiring x402 payment
Malformed or non-HTML URL (e.g. PDF, binary) — may produce garbled or empty text output

Input

Schema · Request body

Field	Type	Description
url	string
overlap	integer
chunk_size	integer

Output

Schema · Response

{
  "type": "json",
  "example": {
    "url": "https://example.com",
    "text": "Hello\nAgent documents.",
    "links": [],
    "document": {
      "text": "Hello\nAgent documents.",
      "chunks": [
        {
          "end": 22,
          "text": "Hello\nAgent documents.",
          "index": 0,
          "start": 0
        }
      ],
      "sha256": "ad5d70c4710c7e4d89fd61cc8a948080c97155017935a37327b3c88d4ed9f88f",
      "entities": {
        "urls": [],
        "dates": [],
        "money": [],
        "emails": [],
        "evm_addresses": []
      },
      "removed_duplicate_lines": 0
    },
    "markdown": "# Hello\n\nAgent documents.",
    "metadata": {
      "meta": {},
      "title": "Example",
      "language": "en",
      "canonical": "",
      "description": ""
    },
    "limitations": [
      "Static HTML only; no JavaScript, CSS visibility, article detection, table reconstruction or microdata parsing."
    ],
    "structured_data": []
  }
}

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