Which integrations are available for this server?

Enables direct parsing and conversion of academic papers from arXiv URLs into structured Markdown for AI analysis. Converts complex document formats including PDF, DOCX, XLSX, and images into clean, structured Markdown optimized for RAG pipelines and AI processing.

How do I use Document Parser MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Document Parser MCP convert this PDF research paper to markdown: https://arxiv.org/pdf/2408.09869" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Document Parser MCP

A Model Context Protocol (MCP) server that provides intelligent document parsing and conversion capabilities using the Docling toolkit. Convert any document (PDF, DOCX, images, audio, etc.) into clean Markdown for AI processing and RAG pipelines.

Features

Universal Document Support: PDFs, Office documents (DOCX/XLSX/PPTX), images, HTML, Markdown, audio files, and more
Multiple Processing Pipelines:
- Standard: Fast, high-quality conversion with advanced layout analysis
- VLM: Vision-language models for complex layouts and handwritten content
- ASR: Automatic speech recognition for audio transcription
Intelligent Auto-Detection: Automatically selects optimal pipeline based on file type
Concurrent Processing: Built-in job queue for handling multiple requests
MCP Integration: Seamless integration with Claude Desktop and other MCP clients
Clean Markdown Output: High-quality structured text ready for AI consumption

Installation

Prerequisites

Python 3.9 or higher
8GB+ RAM recommended

Quick Start

Clone the repository:

git clone <repository-url> cd document-parser-mcp

Create virtual environment:

python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate

Install dependencies:

pip install -r requirements.txt

Install Docling with optional features:

# Core Docling pip install docling # For Apple Silicon MLX acceleration pip install docling[mlx] # Optional OCR engines pip install easyocr

Usage

Running the Server

Start the MCP server:

python -m document_parser

With custom configuration:

python -m document_parser --config /path/to/config.yaml

With debug logging:

python -m document_parser --debug

Configuration

The server is configured via config.yaml. Key settings:

server: name: document-parser-mcp max_concurrent_jobs: 3 job_timeout_seconds: 600 processing: default_pipeline: standard enable_pipeline_auto_detect: true ocr: engine: easyocr languages: [eng] pdf: backend: dlparse_v4 table_accuracy_mode: accurate

See Configuration Guide for detailed options.

MCP Tools

The server provides the following MCP tools:

`parse_document`

Parse any document to Markdown.

Parameters:

source (required): File path or URL to the document
pipeline (optional): Processing pipeline - standard, vlm, or asr
options (optional): Additional processing options

Example:

{ "name": "parse_document", "arguments": { "source": "https://arxiv.org/pdf/2408.09869", "pipeline": "standard" } }

`parse_document_advanced`

Advanced parsing with detailed configuration.

Parameters:

source (required): File path or URL
pipeline (optional): Processing pipeline
ocr_enabled (optional): Enable/disable OCR
table_accuracy_mode (optional): fast or accurate
pdf_backend (optional): PDF processing backend
enable_enrichments (optional): Enable code/formula enrichments

`get_job_status`

Get the status of a processing job.

Parameters:

job_id (required): Job identifier

`list_supported_formats`

List all supported input formats and pipelines.

`get_queue_statistics`

Get current queue and processing statistics.

Integration with Claude Desktop

Add to your Claude Desktop configuration (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{ "mcpServers": { "document-parser": { "command": "python", "args": ["-m", "document_parser"], "cwd": "/path/to/document-parser-mcp" } } }

Restart Claude Desktop and the document parser will be available as a tool.

Pipeline Selection Guide

Standard Pipeline (Default)

Best for: Born-digital PDFs, Office documents, clean layouts
Features: Advanced layout analysis, table structure recovery, optional OCR
Performance: Fast, memory-efficient

VLM Pipeline

Best for: Complex layouts, handwritten notes, screenshots, scanned documents
Features: Vision-language model processing, end-to-end page understanding
Performance: Slower, MLX-accelerated on Apple Silicon

ASR Pipeline

Best for: Audio files (meetings, lectures, interviews)
Features: Whisper-based transcription
Performance: CPU/GPU intensive

Development

Running Tests

# Install development dependencies pip install -r requirements-dev.txt # Run tests pytest # Run with coverage pytest --cov=document_parser

Code Quality

# Format code black document_parser tests # Lint ruff check document_parser tests # Type checking mypy document_parser

Project Structure

document-parser-mcp/ ├── document_parser/ # Main package │ ├── config/ # Configuration system │ ├── core/ # Core exceptions and types │ ├── engine/ # Document processing engine │ ├── mcp/ # MCP server implementation │ ├── processing/ # Job queue and tracking │ └── utils/ # Utility functions ├── tests/ # Test suite ├── config.yaml # Default configuration ├── requirements.txt # Production dependencies └── setup.py # Package configuration

Performance Optimization

Memory Management

Configure max_memory_gb for your system
Set max_concurrent_jobs based on available resources
Large files are processed with automatic cleanup

MLX Acceleration (Apple Silicon)

Install with pip install docling[mlx]
Enable in config: enable_mlx_acceleration: true
Automatic fallback to CPU if unavailable

Troubleshooting

Common Issues

"ModuleNotFoundError: No module named 'docling'"

pip install docling

Queue is full errors

Wait for current jobs to complete
Increase max_concurrent_jobs in config

Memory errors with large files

Reduce max_memory_gb in config
Use pipeline: standard instead of vlm

OCR not working

pip install easyocr # Or for tesseract brew install tesseract # macOS

Contributing

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Make your changes
Add tests for new functionality
Run the test suite
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

License

MIT License - see LICENSE file for details.

Acknowledgments

Built with Docling - an amazing document understanding toolkit
Uses the Model Context Protocol for AI integration

Support

Issues: GitHub Issues
Documentation: Full Documentation

Document Parser MCP

Document Parser MCP

Features

Installation

Prerequisites

Quick Start

Usage

Running the Server

Configuration

MCP Tools

`parse_document`

`parse_document_advanced`

`get_job_status`

`list_supported_formats`

`get_queue_statistics`

Integration with Claude Desktop

Pipeline Selection Guide

Standard Pipeline (Default)

VLM Pipeline

ASR Pipeline

Development

Running Tests

Code Quality

Project Structure

Performance Optimization

Memory Management

MLX Acceleration (Apple Silicon)

Troubleshooting

Common Issues

Contributing

License

Acknowledgments

Support

Resources

New MCP Servers

Latest Blog Posts

MCP directory API