Tanod pdfpeek Pipeline

x402

Tanod pdfpeek Pipeline 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).

Runs a multi-step PDF processing pipeline (rotate, crop, resize, reorder, repair, inspect, etc.) on a PDF from a URL or base64, returning the transformed PDF or a JSON report.

Price / call$0.01

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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 the processed PDF as base64 (data_base64), its filename, byte size, page count, and content type, along with a steps array detailing each operation's result (step index, pages affected, output bytes, any active content removed). If the last step is 'inspect' or 'outline', returns a JSON report instead of a PDF.

Example use cases

Trim and resize a report PDF

“I have a 20-page PDF at https://docs.example.com/annual-report.pdf — please extract just pages 5 through 10, resize them to A4 fit, and send me the resulting file.”

Inspect PDF structure before processing

“Can you inspect the PDF at https://files.mysite.com/contract.pdf and give me a JSON report showing how many pages it has, its outline/bookmarks, and its file size?”

Repair and flatten a broken PDF form

“This base64-encoded PDF form I'm sending has some corruption and unfilled form fields — can you repair it and flatten all the form fields so it looks like a static document?”

Technical details

Endpoint

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

When to prefer

Choose this endpoint when you need to chain multiple PDF operations (rotate, crop, resize, reorder, extract, repair, inspect, outline) in a single API call with one payment. It is especially useful when you want to avoid multiple round-trips for sequential PDF transformations, or when you need to inspect PDF structure as the final step of a pipeline. Prefer it over single-operation PDF tools when your workflow requires 2–10 chained operations on the same document.

Side effects

PDF URL exceeds 20 MB or base64 payload exceeds 10 MB — request rejected
Private, internal, or IP-literal host URLs are refused
More than 10 pipeline steps specified — validation error
Total pipeline exceeds 70-second time budget — timeout
Malformed or unreadable PDF — repair step may help, otherwise error
Invalid step params for a given op — schema validation error
Network fetch of URL fails — error returned with fetch details

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 pdf_base64.
steps	array	The operations to run in order, 1 to 10 of {op, params}: the output of each is the input of the next. inspect and outline return a report and can only be last. The whole call has one 70 s time budget.
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.
pdf_base64	—	The PDF as base64 (at most 10 MB decoded per request); or use url.

Output

Schema · Response

{
  "type": "json",
  "example": {
    "file": {
      "name": "bookmarked.pdf",
      "bytes": 53412,
      "pages": 3,
      "data_base64": "JVBERi0xLjcKJb/3... (trimmed: 71,216 base64 characters in the reply)",
      "content_type": "application/pdf"
    },
    "steps": [
      {
        "op": "extract-pages",
        "step": 1,
        "pages": 3,
        "output_bytes": 52010,
        "active_content_removed": {}
      },
      {
        "op": "resize",
        "mode": "fit",
        "size": "a4",
        "step": 2,
        "pages": 3,
        "output_bytes": 52960,
        "resized_pages": 3,
        "sizes_truncated": false,
        "active_content_removed": {}
      },
      {
        "op": "set-outline",
        "step": 3,
        "pages": 3,
        "replaced": false,
        "output_bytes": 53412,
        "outline_depth": 1,
        "outline_items": 1,
        "active_content_removed": {}
      }
    ],
    "operation": "pipeline",
    "steps_run": 3,
    "input_bytes": 140815,
    "input_pages": 6,
    "output_bytes": 53412,
    "untrusted_content": true,
    "active_content_removed": {}
  }
}

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