Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP-MinerU extract all tables from quarterly_report.pdf using the fastest backend"
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.
MCP server for document and image parsing via MinerU. Extract text, tables, and formulas from PDFs, screenshots, and scanned documents with MLX acceleration on Apple Silicon.
claude mcp add --transport stdio --scope user mineru -- \
uvx --from mcp-mineru python -m mcp_mineru.serverThis command installs and configures the server for all your Claude Code projects using uvx (no manual installation required).
Alternative methods: See Installation Guide for PyPI, source installation, and Claude Desktop configuration.
Related MCP server: PDF MCP Flow
Multiple format support: PDF, JPEG, PNG, and other image formats
OCR capabilities: Built-in text extraction from screenshots and photos
Table recognition: Preserves structure when extracting tables
Formula extraction: Converts mathematical equations to LaTeX
MLX acceleration: Optimized for Apple Silicon (M1/M2/M3/M4)
Multiple backends: Choose speed vs quality tradeoffs
User: "Analyze the tables in research_paper.pdf"
Claude: [Calls parse_pdf tool] "The paper contains 3 tables..."User: "What does this screenshot say? image.png"
Claude: [Calls parse_pdf tool] "The screenshot contains..."User: "Which backend should I use?"
Claude: [Calls list_backends tool] "Your system has Apple Silicon M4..."For more examples, see Usage Examples.
Parse PDF and image files to extract structured content as Markdown.
Parameters:
file_path (required): Absolute path to file (PDF, JPEG, PNG, etc.)
backend (optional): pipeline | vlm-mlx-engine | vlm-transformers
formula_enable (optional): Enable formula recognition (default: true)
table_enable (optional): Enable table recognition (default: true)
start_page (optional): Starting page for PDFs (default: 0)
end_page (optional): Ending page for PDFs (default: -1)
Check system capabilities and get backend recommendations.
Returns: System information, available backends, and performance recommendations.
PDF documents (.pdf)
JPEG images (.jpg, .jpeg)
PNG images (.png)
Other image formats (WebP, GIF, etc.)
Benchmarked on Apple Silicon M4 (16GB RAM):
pipeline: ~32s/page, CPU-only, good quality
vlm-mlx-engine: ~38s/page, Apple Silicon optimized, excellent quality
vlm-transformers: ~148s/page, highest quality, slowest
Installation Guide - Detailed installation options
Updating Guide - How to update to the latest version
Usage Examples - More use cases and API reference
MinerU Documentation - Underlying parsing engine
git clone https://github.com/TINKPA/mcp-mineru.git
cd mcp-mineru
uv pip install -e ".[dev]"
# Run tests
pytest
# Format code
black src/
ruff check src/Apache License 2.0 - see LICENSE file for details.
Built on top of MinerU by OpenDataLab.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Turn any PDF into structured JSON via AI + OCR: invoices, bank statements, contracts.
OCR, transcription, file extraction, and image generation for AI agents via MCP.
Search arXiv/Semantic Scholar/OpenAlex + medical evidence (PubMed/Europe PMC) + LaTeX/PDF tools.
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