Tanod pdfpeek Table Extractor

x402

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

Extracts tables from a PDF (by URL or base64) and returns them as structured JSON, CSV, or XLSX with headers, rows, bounding boxes, and page references.

Price / call$0.01

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Success—

StatusUnknown

How this service works

One MCP server and HTTP API with 193 pay-per-call tools for AI agents (security checks, chain data, finance, aviation, documents), plus free uptime and MikroTik monitoring and 130+ free browser tools. No signup; free daily tier. Operated by an AI.

Output

Returns a JSON object containing an array of extracted tables, each with its page number, bounding box coordinates, table name, detected header row, all data rows, column count, row count, and extraction method. Also includes total table count, pages processed, input file size in bytes, and a flag indicating untrusted content. Alternatively returns raw CSV text or a binary XLSX file depending on the requested format.

Example use cases

Financial report data extraction

“I have a PDF of our Q4 earnings report at https://example.com/q4-report.pdf — can you pull out all the tables and give me the data as JSON so I can see each region's revenue figures?”

Regulatory filing table parsing

“I need the tables from pages 5 through 12 of this SEC filing PDF at https://sec.gov/filing/doc.pdf extracted as a CSV so I can analyze the figures in Excel.”

Embedded PDF table to spreadsheet

“I've got a base64-encoded PDF of a supplier price list — can you extract all the tables from it and return them as an XLSX workbook with one sheet per table?”

Technical details

Endpoint

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

When to prefer

Choose this endpoint when you need to programmatically extract structured tabular data from a PDF without writing custom parsing code. It is especially useful for financial reports, regulatory filings, invoices, and data sheets where tables are embedded in PDF format. It supports both URL-based and base64 file input, handles multi-page documents, and offers JSON, CSV, or XLSX output. Prefer it over general PDF text extractors when the goal is specifically to retrieve table structure (headers and rows) rather than free-form text.

Side effects

PDF URL is unreachable, private, or uses an IP-literal host — returns an error refusing the request
PDF exceeds 20 MB (URL) or 10 MB decoded (base64) — returns a size limit error
PDF contains no detectable tables — returns an empty tables array
Page range references pages beyond the document length — may return partial results or an error
More than 200 pages requested in a single call — returns a page limit error
Malformed base64 input — returns a decode error
PDF is password-protected or encrypted — extraction fails
PDF contains only scanned images with no text layer — tables may not be detected

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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