Tanod Docs PDF Table Extractor

x402

Tanod Docs PDF Table Extractor is a paid API for AI agents from docs.tanod.dev, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-13).

Extracts tables from a PDF file and returns them as structured JSON, CSV, or Excel (XLSX) with per-table page, bounding box, headers, and row data.

Price / call$0.01

Seller volume →—

Success—

StatusUnknown

How this service works

Documents: DOCX template fill, Office to PDF, any file to Markdown, PDF to Word, OCR, PDF pipeline.

Output

A JSON response containing the operation type, input byte size, page count, and a `tables` array. Each table entry includes its page number, bounding box coordinates, auto-generated name, detection method, column count, row count, a `header` array of column names, and a `rows` array of string arrays. For CSV or XLSX output formats, the file is returned either base64-encoded inline or as a time-limited download URL with expiry, byte size, content type, and SHA-256 hash.

Example use cases

Extract financial data from annual report

“Can you extract all the tables from this annual report PDF — https://example.com/annual-report-2024.pdf — and return the data as JSON? I want to see the headers and row values for each table.”

Convert PDF tables to Excel for analysis

“I have a PDF with several data tables at https://example.com/survey-results.pdf — can you extract just pages 3 through 7 and export them into an XLSX file so I can open them in Excel?”

Grab specific page tables as CSV

“There's a PDF at https://example.com/q3-metrics.pdf with a big table on page 2. Can you extract that single page as a CSV and give me a download link instead of sending the whole thing inline?”

Technical details

Endpoint

POSThttps://docs.tanod.dev/v1/pdf/tables?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need to extract structured tabular data from a PDF — especially for reports, financial documents, or data exports where you need headers and rows. It is purpose-built for PDF table detection and returns rich metadata (page, bounding box, header detection) in multiple output formats (JSON, CSV, XLSX). Prefer it over generic PDF-to-text or OCR endpoints when the output must be machine-readable structured tables rather than raw text.

Side effects

PDF URL is inaccessible, private, or internal (IP-literal hosts refused)
File exceeds 20 MB via URL or 10 MB via base64
No tables detected in the specified pages
Page range is invalid or exceeds 200-page limit
PDF is scanned/image-only with no extractable text (pages_without_text returned)
Invalid base64 encoding for file_base64 input
Unsupported format or output enum value

Input

Schema · Request body

Field	Type	Description
url	—	Public http(s) URL of the PDF (at most 20 MB; private, internal and IP-literal hosts are refused); or use file_base64.
pages	—	Pages to read (default all; at most 200 per call): N, N-M, N- (to the end), -M, last; comma separated, e.g. 1-5,9.
format	string	json (default: every table with its page, box, header and rows), csv (one CSV file per table) or xlsx (one workbook, a sheet per table).
output	string	base64 (default): the file comes back inline as base64. url: the file is stored for 24 hours and the reply carries a download link (url, expires_at, bytes, content_type, sha256) instead of the base64, which keeps the file out of your context window.
file_base64	—	The PDF as base64 (at most 10 MB decoded); or use url.

Output

Schema · Response

{
  "type": "json",
  "example": {
    "format": "json",
    "tables": [
      {
        "bbox": [
          56,
          130,
          536,
          262
        ],
        "name": "page1-table1",
        "page": 1,
        "rows": [
          [
            "North",
            "1,204",
            "1,311",
            "1,402",
            "1,590",
            "5,507"
          ],
          [
            "South",
            "980",
            "1,015",
            "1,120",
            "1,205",
            "4,320"
          ],
          [
            "East",
            "1,512",
            "1,498",
            "1,630",
            "1,774",
            "6,414"
          ],
          [
            "West",
            "866",
            "902",
            "951",
            "1,040",
            "3,759"
          ],
          [
            "Total",
            "4,562",
            "4,726",
            "5,103",
            "5,609",
            "20,000"
          ]
        ],
        "index": 1,
        "header": [
          "Region",
          "Q1",
          "Q2",
          "Q3",
          "Q4",
          "Total"
        ],
        "method": "lines",
        "columns": 6,
        "row_count": 5
      }
    ],
    "operation": "tables",
    "input_bytes": 2963,
    "input_pages": 1,
    "table_count": 1,
    "pages_processed": 1,
    "untrusted_content": true,
    "pages_without_text": []
  }
}

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