io.github.diyaluo/pdf-extract-mcp

pdf-extract-mcp

A Model Context Protocol (MCP) server that extracts tables and text from digital (text-layer) PDFs — bank statements, invoices, bills — into CSV / JSON / Excel, with automatic PII redaction.

Part of the invoice-extractor product. This package is the MCP distribution channel ("ecosystem parasite": one backend, many host workflows). Web demo lives in the parent project.

What it does

• extract_pdf — upload a digital PDF (base64 or URL), get back structured table rows as CSV / JSON records / XLSX.
• health — service status and free-tier limits.

Table detection tries three strategies per page: ruled tables → explicit columns derived from the header row's own x-positions (handles statement PDFs with no grid lines, the common case for Western banks) → generic text alignment.

PII redaction (on by default) covers:

Bank identifiers that are ambiguous as bare numbers (routing/account/sort code) are masked only when they follow a label, so reference numbers and amounts stay readable. Amounts, dates and descriptions are never altered.

Scanned / image-only PDFs are explicitly rejected (SCANNED_PDF_UNSUPPORTED) — the free tier covers digital PDFs only (OCR is a paid-tier candidate).

Limits (free tier)

• 100 pages / rolling 30 days / client (anonymous, keyed by client hash).
• 50 MB per file.
• Above quota the tool returns a structured QUOTA_EXCEEDED response with an in-band upgrade pointer. It never silently bills.
• Set PDF_EXTRACT_API_KEY to a paid key to bypass the quota (billing gateway is a P1 item; current free tier has no paid keys issued).

Run (local stdio — Claude Desktop / Cursor / any MCP client)

pip install -e .
pdf-extract-mcp

Client config example:

{
  "mcpServers": {
    "pdf-extract": {
      "command": "pdf-extract-mcp",
      "env": {}
    }
  }
}

Run (remote streamable-http, for hosted MCP clients)

REMOTE_TRANSPORT=http MCP_HOST=127.0.0.1 MCP_PORT=5060 \
  python -m pdf_extract_mcp.server
# POST JSON-RPC to http://127.0.0.1:5061/mcp

Tool schema

extract_pdf(
  pdf_base64: str | None,     # preferred: PDF bytes as base64
  pdf_url:    str | None,     # alternative: direct https .pdf URL
  output:     "csv"|"json"|"xlsx",   # default csv
  redact_pii: bool = true,
  api_key:    str | None,    # paid key, optional
) -> {
  ok, type: "tables"|"text", pages, tables, rows, columns,
  csv | rows_json | xlsx_base64,   # depends on output
  elapsed_ms
}

Telemetry

Every call is appended (best-effort, never blocks extraction) to ~/.cache/pdf-extract-mcp/mcp_usage.jsonl (override with PDF_EXTRACT_STATE_DIR). Clients are recorded as salted hashes; no PDF content and no PII are ever logged.

Note: as a distributed stdio bundle this file lives on the user's machine, so the publisher cannot read it. Real external usage is read from the hosting registry's owner API instead (GET /servers/{namespace}/{name}/logs on Smithery returns the invocation count).

Stack

mcp >= 1.2 (works on both mcp 1.x FastMCP and mcp 2.x MCPServer), pdfplumber, openpyxl. pandas is optional — when absent the package falls back to a lightweight list-of-rows table (stdlib CSV/JSON + openpyxl), which keeps the distributable bundle small. Python 3.10+.

License

MIT