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Pocket Network Table Extraction
x402
Pocket Network Table Extraction is a paid API for AI agents from agent.pocket.network, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-02).
Extracts structured tables from document text into typed JSON with headers, rows, and cell types, paid per-request in USDC with no API key required.
Price / call$0.005
Success—
StatusUnknown
How this service works
Extract tables from documents into typed JSON: headers, rows, and cell types. POST /v1/table with {text} and get the parsed tables as one JSON object. Deterministic on clean inputs. Pay per request in USDC; no account, no API key.
Output
Returns a single JSON object containing all parsed tables found in the input text. Each table includes an array of headers (column names), an array of rows (each row being an array of cell values), and cell type annotations indicating the data type (string, number, currency, etc.) for each column or cell.
Example use cases
Financial report table parsing
“I've got the raw text from a quarterly earnings report — can you pull out all the tables, like the income statement and balance sheet, and give me the data as structured JSON with typed cells?”
Research paper data extraction
“Here's the text of a scientific paper. Can you find and extract all the data tables in it, returning each table's headers and rows as JSON so I can analyze the numbers?”
Government document table structuring
“I have a block of text copied from a government budget document. Can you extract every table in it — including column headers and row values — and return them as typed JSON objects?”
Technical details
Endpoint
POSThttps://agent.pocket.network/v1/table-extraction?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need deterministic, structured extraction of tabular data from plain text — especially from document extracts, reports, or any text containing rows and columns. It is ideal for agentic workflows that need typed JSON (not raw string scraping) and where no API key or account setup is desired. Prefer it over general-purpose LLM parsing when consistency and cost predictability matter, and over OCR-based table tools when you already have the text content.
Side effects
- Input text contains no recognizable tables — returns empty tables array
- Malformed or ambiguous table formatting may cause incorrect row/column alignment
- Very large documents may time out or produce truncated results
- Non-Latin character sets or complex nested tables may parse incorrectly
- Missing or empty 'text' field returns a validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | Text to extract tables from. |
Output
Schema · Response
{
"type": "json",
"example": {
"data": {},
"portal": {
"serviceId": "table-extraction",
"provenance": "third-party-supplier",
"schemaCheck": "passed"
}
}
}Tags
x402
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