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NetIntel PDF Parse
x402
NetIntel PDF Parse is a paid API for AI agents from netintel.dev, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-11).
Fetches and parses a PDF from a URL or base64 input, returning full Markdown text, per-page statistics, document metadata, and scanned-page detection
Price / call$0.005
Success—
StatusUnknown
How this service works
Network intelligence API — 142 endpoints, all pay-per-call via x402 micropayments (USDC on Base or Solana mainnet). NetIntel is agent fair-trade aligned: transparent per-call pricing in USDC on Base or Solana via x402, no API keys or signup, and automatic no-charge on server errors, upstream failures, and input-validation rejections — failed calls are never billed across NetIntel's network, domain, and data-intelligence endpoints.
Output
A JSON object containing: full Markdown representation of the parsed text (with page separators), an array of per-page objects with character ranges, word counts, and text-presence flags, a list of scanned (image-only) pages, document metadata (title, author, creation date, PDF version, form presence), source info (URL, byte size), total word/character counts, pages parsed, and a truncated flag indicating if the output was capped.
Example use cases
Extract financial report data
“I need the text from this SEC filing PDF at https://example.com/10k-filing.pdf — can you parse pages 1-10 and give me the markdown content so I can analyze the financials?”
Check if PDF is scanned or searchable
“Can you parse this PDF at https://contracts.example.com/lease-agreement.pdf and tell me if it has any scanned pages that might not have selectable text?”
Parse uploaded document as base64
“Here's a PDF encoded in base64 — can you extract all the text from it and return it as markdown with the page-by-page word counts?”
Technical details
Endpoint
POSThttps://netintel.dev/pdf/parse?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to extract readable text and metadata from a PDF document — whether publicly hosted or provided as base64 — and want structured per-page output alongside Markdown. It handles both native-text and scanned PDFs (flagging image-only pages), supports page-range selection, and charges only on successful parses. Prefer it over generic web scrapers when the target is definitively a PDF file.
Side effects
- PDF URL not publicly accessible or returns non-200 — fetch failure error
- PDF exceeds 10 MB size limit — rejected before billing
- base64 payload exceeds ~700 KB — rejected before billing
- Malformed or password-protected PDF — parse error returned
- Both url and file_base64 supplied simultaneously — input validation rejection (not billed)
- Upstream fetch timeout — server error, not billed
- Invalid page range string — input validation rejection, not billed
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| url | string | Public URL of the PDF (up to 10 MB) |
| pages | string | Pages to parse, 1-based, e.g. "1-3,7" (default: the first 50) |
| max_chars | number | Cap on the markdown length (default 100000) |
| file_base64 | string | The PDF as base64 or a data: URL (up to ~700 KB). Send url OR file_base64. |
Output
Schema · Response
{
"type": "json",
"example": {
"pages": [
{
"page": 1,
"char_end": 507,
"has_text": true,
"char_start": 0,
"word_count": 76
},
{
"page": 2,
"char_end": 581,
"has_text": false,
"char_start": 509,
"word_count": 0
},
{
"page": 3,
"char_end": 730,
"has_text": true,
"char_start": 583,
"word_count": 19
}
],
"source": {
"url": "https://netintel.dev/samples/pdf-parse-sample.pdf",
"type": "url",
"bytes": 2236,
"final_url": "https://netintel.dev/samples/pdf-parse-sample.pdf"
},
"findings": [
"scanned_pages_present"
],
"markdown": "<!-- page 1 -->\n\n## Quarterly Network Report\n\nThis sample shows what POST /pdf/parse returns: paragraphs rebuilt from the text layer, ...",
"metadata": {
"title": "Quarterly Network Report (sample)",
"author": "NetIntel",
"created": "2026-10-10T00:00:00Z",
"has_form": false,
"pdf_version": "1.4"
},
"truncated": false,
"char_count": 730,
"page_count": 3,
"word_count": 95,
"pages_parsed": 3,
"scanned_pages": [
2
]
}
}Tags
x402
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