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TableGrab
x402
TableGrab is a paid API for AI agents from priceplain.onrender.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-29).
Extracts clean, typed JSON tables or Markdown content from a public URL (HTML, CSV, XLSX, or text PDF)
Price / call$0.01
Success—
StatusUnknown
How this service works
TableGrab: give a public URL and get clean, typed JSON tables (headers, row objects, inferred column types, caption/heading context; colspan/rowspan handled; ISO dates and real numbers) or the page's main content as Markdown with title, byline, date and links. Handles HTML, CSV, XLSX and text PDFs. SSRF-safe, no JS rendering (returns js_required). $0.01 per call. Feedback/support: POST /feedback (free) or js4336638@gmail.com.
Output
Returns a JSON object with a source URL, the mode used (tables or markdown), input type (html/csv/xlsx/pdf), and either: an array of table objects each containing rows (as typed key-value objects), column definitions (key, type, header), n_rows, n_cols, caption, context heading, and a truncated flag — or Markdown text with title, byline, date, and links for article/page content.
Example use cases
Wikipedia table extraction for analysis
“Pull the tables from https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal) and give me clean JSON with inferred column types so I can analyze the GDP figures.”
Government PDF data extraction
“Extract the data tables from this public PDF — https://example.gov/annual-report-2023.pdf — and return them as typed JSON rows with headers.”
News article content as Markdown
“Fetch the main article content from https://www.bbc.com/news/some-article and give it back as Markdown with the title, byline, and any links included.”
Technical details
Endpoint
POSThttps://priceplain.onrender.com/v1/extract?utm_source=zero.xyzWhen to prefer
Choose TableGrab when you need to extract well-structured, typed JSON tables from any public URL — especially HTML pages with complex colspan/rowspan, CSVs, XLSX files, or text-based PDFs — and want normalized output (ISO dates, real numbers, null handling, snake_case keys) without running your own scraping infrastructure. It also handles article-to-Markdown conversion with metadata. Prefer it over generic scraping tools when column type inference and clean cell normalization matter, or when the source is a spreadsheet or PDF rather than pure HTML.
Side effects
- js_required returned when page needs JavaScript rendering (no headless browser)
- URL exceeds 5 MiB size limit
- Non-public or auth-gated URLs fail to fetch
- Scanned/image PDFs return no tables (text PDF only)
- Payment failure or insufficient USDC balance returns 402
- Malformed or non-URI input returns validation error
- No tables found on page with mode=tables returns empty tables array
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| url | string | public https URL of an HTML page, CSV, XLSX or text PDF (max 5 MiB) |
| mode | string | tables = JSON tables; markdown = main article content; auto = tables if any, else markdown |
| clean | boolean | normalize cells: ISO dates, real numbers, nulls, whitespace; leading-zero codes kept; snake_case keys |
| max_tables | integer |
Output
Schema · Response
{
"type": "json",
"example": {
"source": {
"url": "https://en.wikipedia.org/wiki/List_of_United_States_cities_by_population"
},
"tables": [
{
"rows": [
{
"city": "New York",
"state": "New York",
"c_2024_estimate": 8478072
}
],
"index": 0,
"n_cols": 3,
"n_rows": 1,
"source": {
"type": "html",
"table_index": 3
},
"caption": null,
"columns": [
{
"key": "city",
"type": "text",
"header": "City"
},
{
"key": "state",
"type": "text",
"header": "State"
},
{
"key": "c_2024_estimate",
"type": "integer",
"header": "2024 estimate"
}
],
"context": "List of United States cities by population",
"headers": [
"City",
"State",
"2024 estimate"
],
"truncated": false
}
],
"mode_used": "tables",
"input_type": "html",
"tables_found": 9
}
}Tags
x402
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