Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Developer Tools
Mineru Pdf
Parse PDF documents with MinerU MCP to extract text, tables, and formulas. Supports multiple backends including MLX-accelerated inference on Apple Silicon.
skill openclawclawhub Free
Parse PDF documents with MinerU MCP to extract text, tables, and formulas. Supports multiple backends including MLX-accelerated inference on Apple Silicon.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, parse.py, test.sh
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- Etoile04
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
MinerU PDF Parser
Parse PDF documents using MinerU MCP to extract structured content including text, tables, and formulas with MLX acceleration on Apple Silicon.
Option 1: Install MinerU MCP (for Claude Code)
claude mcp add --transport stdio --scope user mineru -- \ uvx --from mcp-mineru python -m mcp_mineru.server This installs and configures MinerU for all Claude projects. Models are downloaded on first use.
Option 2: Use Direct Tool (preserves files)
The skill includes a direct parsing tool that saves output to a persistent directory: python /Users/lwj04/clawd/skills/mineru-pdf/parse.py <pdf_path> <output_dir> [options] Advantages: ✅ Files are saved permanently (not auto-deleted) ✅ Full control over output location ✅ No MCP overhead ✅ Works with any Python environment that has MinerU
Method 1: Using the Direct Tool (Recommended)
# Parse entire PDF python /Users/lwj04/clawd/skills/mineru-pdf/parse.py \ "/path/to/document.pdf" \ "/path/to/output" # Parse specific pages python /Users/lwj04/clawd/skills/mineru-pdf/parse.py \ "/path/to/document.pdf" \ "/path/to/output" \ --start-page 0 --end-page 2 # Use Apple Silicon optimization python /Users/lwj04/clawd/skills/mineru-pdf/parse.py \ "/path/to/document.pdf" \ "/path/to/output" \ --backend vlm-mlx-engine # Text only (faster) python /Users/lwj04/clawd/skills/mineru-pdf/parse.py \ "/path/to/document.pdf" \ "/path/to/output" \ --no-table --no-formula
Parse a PDF document
uvx --from mcp-mineru python -c " import asyncio from mcp_mineru.server import call_tool async def parse_pdf(): result = await call_tool( name='parse_pdf', arguments={ 'file_path': '/path/to/document.pdf', 'backend': 'pipeline', 'formula_enable': True, 'table_enable': True, 'start_page': 0, 'end_page': -1 # -1 for all pages } ) if hasattr(result, 'content'): for item in result.content: if hasattr(item, 'text'): print(item.text) break asyncio.run(parse_pdf()) "
Check system capabilities
uvx --from mcp-mineru python -c " import asyncio from mcp_mineru.server import call_tool async def list_backends(): result = await call_tool( name='list_backends', arguments={} ) if hasattr(result, 'content'): for item in result.content: if hasattr(item, 'text'): print(item.text) break asyncio.run(list_backends()) "
parse_pdf
Required: file_path - Absolute path to the PDF file Optional: backend - Processing backend (default: pipeline) pipeline - Fast, general-purpose (recommended) vlm-mlx-engine - Fastest on Apple Silicon (M1/M2/M3/M4) vlm-transformers - Slowest but most accurate formula_enable - Enable formula recognition (default: true) table_enable - Enable table recognition (default: true) start_page - Starting page (0-indexed, default: 0) end_page - Ending page (default: -1 for all pages)
list_backends
No parameters required. Returns system information and backend recommendations.
Extract tables from a specific page range
uvx --from mcp-mineru python -c " import asyncio from mcp_mineru.server import call_tool async def parse_pdf(): result = await call_tool( name='parse_pdf', arguments={ 'file_path': '/path/to/document.pdf', 'backend': 'pipeline', 'table_enable': True, 'start_page': 5, 'end_page': 10 } ) if hasattr(result, 'content'): for item in result.content: if hasattr(item, 'text'): print(item.text) break asyncio.run(parse_pdf()) "
Parse with formula recognition only (faster)
uvx --from mcp-mineru python -c " import asyncio from mcp_mineru.server import call_tool async def parse_pdf(): result = await call_tool( name='parse_pdf', arguments={ 'file_path': '/path/to/document.pdf', 'backend': 'vlm-mlx-engine', 'formula_enable': True, 'table_enable': False # Disable for speed } ) if hasattr(result, 'content'): for item in result.content: if hasattr(item, 'text'): print(item.text) break asyncio.run(parse_pdf()) "
Parse single page (fastest for testing)
uvx --from mcp-mineru python -c " import asyncio from mcp_mineru.server import call_tool async def parse_pdf(): result = await call_tool( name='parse_pdf', arguments={ 'file_path': '/path/to/document.pdf', 'backend': 'pipeline', 'formula_enable': False, 'table_enable': False, 'start_page': 0, 'end_page': 0 } ) if hasattr(result, 'content'): for item in result.content: if hasattr(item, 'text'): print(item.text) break asyncio.run(parse_pdf()) "
Performance
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 Note: First run downloads models (can take 5-10 minutes). Models are cached in ~/.cache/uv/ for faster subsequent runs.
Output Format
Returns structured Markdown with: Document metadata (file, backend, pages, settings) Extracted text with preserved structure Tables formatted as Markdown tables Formulas converted to LaTeX
Supported Formats
PDF documents (.pdf) JPEG images (.jpg, .jpeg) PNG images (.png) Other image formats (WebP, GIF, etc.)
Module not found error
If you get "No module named 'mcp_mineru'", make sure you installed it: claude mcp add --transport stdio --scope user mineru -- \ uvx --from mcp-mineru python -m mcp_mineru.server
Slow processing on first run
This is normal. MinerU downloads ML models on first use. Subsequent runs will be much faster.
Timeout errors
Increase timeout for large documents or use smaller page ranges for testing.
Notes
Output is returned as Markdown text Tables are preserved in Markdown format Mathematical formulas are converted to LaTeX Works with scanned documents (OCR built-in) Optimized for Apple Silicon (M1/M2/M3/M4) with MLX backend
Why Files Get Deleted (MCP Method)
The MinerU MCP server uses Python's tempfile.TemporaryDirectory(), which automatically deletes files when the context exits. This is by design to prevent temporary files from accumulating.
How to Preserve Files
Method A: Use the Direct Tool (Recommended) The skill provides parse.py which saves files to a persistent directory: python /Users/lwj04/clawd/skills/mineru-pdf/parse.py \ /path/to/input.pdf \ /path/to/output_dir Advantages: ✅ Files are never auto-deleted ✅ Full control over output location ✅ Can be used in batch processing ✅ No MCP connection needed Generated Structure: /path/to/output_dir/ ├── input.pdf_name/ │ └── auto/ # or vlm/ depending on backend │ ├── input.pdf_name.md │ └── images/ │ └── *.jpg └── input.pdf_name_parsed.md # Copy at root for easy access Method B: Redirect MCP Output If using the MCP method, capture the output and save it: # Capture to file claude -p "Parse this PDF: /path/to/file.pdf" > /tmp/output.md # Or use within a script that saves the result
Comparison
FeatureDirect ToolMCP MethodFiles persisted✅ Yes❌ No (auto-deleted)Custom output dir✅ Yes❌ No (temp only)Claude Code integration⚠️ Manual✅ NativeSpeed✅ Fast⚠️ MCP overheadOffline use✅ Yes⚠️ Needs Claude Code
Recommendation
Use Direct Tool when you need to keep the files for later use Use MCP Method when working within Claude Code and only need the text content
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Scripts1 Docs
- SKILL.md
- parse.py
- test.sh