GroupDocs.Parser MCP Server Leave feedback

GroupDocs.Parser MCP server lets AI agents like Claude, Cursor, and Copilot pull data out of documents — text, tables, images, metadata, barcodes — from PDF, Office, email, e-book, and image formats, locally on your machine. Install with one command:

docker run --rm -i -v $(pwd)/documents:/data \
  ghcr.io/groupdocs-parser/parser-net-mcp:latest

Note

This product ships via Docker only. GroupDocs.Parser’s engine embeds around 234 MB of ONNX models, which puts the packed tool over nuget.org’s 250 MB package limit — so this product ships via Docker only, and there is no dnx command for it. Other GroupDocs MCP servers offer a NuGet (dnx) channel; this one does not, and every command in these pages reflects that. Details: MCP server for .NET.

Or use the guided installer to register the server in your AI client, verify the setup, and configure shared folders in one pass.

Seven tools (full details in the tools reference):

• extract_text — plain text, whole document or one page.
• extract_tables — tables as Markdown that renders in chat, or JSON rows to compute with.
• extract_metadata — author, title, dates, custom properties, EXIF/XMP/IPTC.
• extract_images — embedded images saved to storage as files.
• extract_barcodes — Code128, QR, PDF417, DataMatrix, EAN, UPC, Aztec and more — including on scans.
• get_document_info — type, pages, size, without extracting.
• get_license_status — active licensing mode and metered consumption.

Ask in plain language — “what does this invoice say, and what is in its table?” — and the agent picks the tools.

Installation, prerequisites, and client configuration are platform-specific; the tools and licensing model below are the same everywhere.

The distinction that decides which GroupDocs server you want:

Plenty of pipelines use two: convert for ingestion, parse for extraction.

A scanned page has no text layer, so extract_text returns little — but the engine’s models detect codes in the rasterized image, so extract_barcodes still returns the tracking number on a scanned delivery note. That capability is also why the image is large: the models ship inside it.

There is no OCR: printed words in a scan stay pixels.

Exact config blocks for every client: Register in AI clients.

The server uses MCP’s local stdio transport: your AI client starts the container as a child process and talks to it over standard input/output. No inbound ports, no external endpoints, no telemetry — the data path is agent → local server → local filesystem. Extraction is where document content most obviously leaves a building; here it does not. Details: On-premise architecture.

Scripts that pull text from PDFs are easy to find and hard to trust across formats. Choose this server when you need: one interface across 50+ formats including email and e-books; tables as tables, not as a soup of positioned strings; barcode decoding that works on scans; metadata across documents and images in the same call; and the fidelity of the commercial GroupDocs engine trusted by enterprise teams for over a decade.

• Quick start · Use cases · Troubleshooting & FAQ
• GitHub: server source · installer · integration tests
• Docker image · MCP Registry
• Questions: Parser forum