Sırdaş

MCP · CLI · Python · n8n · TypeScript · Docker

The document layer for your AI agents

PDFs, scans, Word, Excel, PowerPoint, email and photos turned into data an agent can cite: document type, fields with page and position, tables, forms, chunks and datasets. On your machine: no uploads, no credits, no queue.

Using an AI agent?

Copy these instructions into Claude, ChatGPT or Cursor. The agent reads our Markdown docs, installs itself and tells you what it can do.

llms.txt

Are you an agent?

This whole site has a plain-text version:

Install it where you already work

One line. Then ask it to “read invoice.pdf”.

claude mcp add sirdas -- npx -y @sirdaspdf/mcp

One call does it all

read_document unlocks, runs OCR when needed, identifies, extracts with citations, anonymizes, chunks and tells you what to offer next. This is what it returns:

{
  "status": "ok",
  "format": "pdf",
  "document_type": "factura",
  "steps": ["Extracted text from 2 page(s).", "Identified as factura (match 1).", "Extracted 8 field(s) with page citations.", "Replaced 3 personal data item(s) with placeholders."],
  "fields": [
    { "name": "total", "value": "1.428.000", "page": 2,
      "evidence": "Total a pagar: $ 1.428.000", "bbox": [0.08, 0.61, 0.43, 0.63] },
    { "name": "cufe", "value": "8f3a2b1c…", "page": 1, "bbox": [0.08, 0.22, 0.71, 0.24] }
  ],
  "chunks": [{ "id": "fnv1a:9c1e…", "pages": [1, 2], "section": ["Factura"], "tokens": 412, "text": "…" }],
  "next_steps": [{ "tool": "pdf_tables", "why": "Export the invoice line items to CSV." }],
  "bytes_sent": 0
}

Almost any file goes in

  • PDF and scanned PDF
  • PNG and JPG
  • Word (.docx), .odt, .rtf
  • Excel (.xlsx), .ods, CSV
  • PowerPoint (.pptx), .odp
  • EPUB and HTML
  • .eml emails
  • JSON, Markdown, text

Out in the format your stack already uses

  • Chat (OpenAI, TRL)
  • Alpaca (Axolotl, LLaMA-Factory, Unsloth)
  • ShareGPT
  • LangChain Documents
  • LlamaIndex TextNodes
  • Embeddings (id + text + metadata)
  • Hugging Face dataset card
  • train / validation / test by document

Why local

Provable privacy

Medical records, contracts and payroll never leave the machine. The MCP server opens no network connections.

No credits, no queue

Cloud parsers charge per page. Here 100,000 pages cost the same as one.

Every value is checkable

Each field carries its source line and its box on the page: the agent cites, it doesn't guess.

What it doesn't do yet

Reading handwriting, turning charts into data or detecting a pen-ticked box on a scan needs a vision model, and we don't claim it. Word, Excel and PowerPoint are read for content, not re-rendered with their layout.

Full documentation