Manages environment variables for the MCP server configuration, particularly for storing paths to dependencies like Poppler.
Supports project installation via git clone, allowing users to obtain the server code from a repository.
Converts various document formats to markdown for consistent representation, making document content accessible in a standardized format.
Used for image processing operations when handling scanned documents, supporting the OCR functionality to extract text from images.
The server requires Python 3.13 or higher as its runtime environment, with the codebase implemented in Python.
📚 Local Documents MCP Server
A Model Context Protocol (MCP) server for interacting with local documents on Windows systems. This server provides tools to list, load, and process documents with support for OCR on scanned PDFs.
✨ Features
- 📁 Document Discovery: List all documents in a specified directory
- ⚡ Document Processing: Convert various document formats to markdown
- 🔍 OCR Support: Extract text from scanned PDFs using Tesseract OCR
- 🎯 Token Management: Automatic content truncation based on token limits
- 📄 Multi-format Support: Handle Word docs, PDFs, PowerPoint, Excel, and more
🛠️ Tools Available
list_documents
: Find documents by path, name, and extensionload_documents
: Extract document content as markdownload_scanned_document
: Extract text from scanned PDFs using OCR
💻 System Requirements
- Operating System: Windows 10/11
- Python: 3.13 or higher
- Package Manager: uv (recommended)
📋 Prerequisites Installation
1. 🐍 Python 3.13
Download and install Python 3.13 from python.org
2. ⚡ UV Package Manager
Install uv using pip:
3. 📖 Poppler for Windows
Purpose: Required for PDF processing and conversion to images for OCR.
- Download the latest Poppler Windows release from: https://github.com/oschwartz10612/poppler-windows/releases/
- Extract the ZIP file to:
- The Poppler binaries should be located at:
Alternative locations: You can install Poppler in any directory, just make sure to update the .env
file with the correct path.
4. 👁️ Tesseract OCR
Purpose: Required for extracting text from scanned documents and images.
- Download Tesseract for Windows from: https://github.com/UB-Mannheim/tesseract/wiki
- Install Tesseract following the installer instructions
- Make sure Tesseract is added to your system PATH, or note the installation directory
🚀 Project Installation
1. 📥 Clone or Download the Project
2. 📦 Install Python Dependencies
This will install all required dependencies from pyproject.toml
:
markitdown[docx,pdf,pptx,xls,xlsx]>=0.1.2
- Document conversionmcp[cli]>=1.10.1
- MCP server frameworkopencv-python>=4.11.0.86
- Image processingpdf2image>=1.17.0
- PDF to image conversionpytesseract>=0.3.13
- Tesseract OCR wrapperpython-dotenv>=1.1.1
- Environment variable managementtiktoken>=0.9.0
- Token counting
3. ⚙️ Configure Environment Variables
Create or update the .env
file in the project root:
Note: Update the path to match your Poppler installation location.
🔧 Configuration for MCP Clients
🤖 Claude Desktop Configuration
Add the following configuration to your Claude Desktop config.json
file:
- First argument: Path to your documents directory
- Example:
"C:\\Users\\YourUsername\\Documents\\MyDocuments"
- Use double backslashes for Windows paths in JSON
- Example:
- Second argument: Maximum tokens per document
- Example:
"30000"
- Adjust based on your needs and Claude's token limits
- Example:
📝 Example Configurations
For different document locations:
🎯 Usage
🚀 Starting the Server
The server is automatically started when Claude Desktop loads with the configured settings.
🔄 Available Operations
- 📋 List Documents: Discover all documents in your configured directory
- 📄 Load Standard Documents: Process Word docs, PDFs, PowerPoint, Excel files
- 🔍 Load Scanned Documents: Use OCR to extract text from scanned PDFs
📊 Response Format
The server returns structured responses with:
- Document paths and metadata
- Token usage information
- Processing time (for OCR operations)
- Extracted content in markdown format
🛠️ Troubleshooting
⚠️ Common Issues
- 🔍 Poppler not found
- Verify Poppler installation path
- Check
.env
file configuration - Ensure path uses double backslashes in Windows
- 👁️ Tesseract not found
- Verify Tesseract installation
- Add Tesseract to system PATH
- Restart command prompt/PowerShell
- 🔐 Permission denied errors
- Ensure the document directory is accessible
- Check file permissions
- Run as administrator if necessary
- ❌ Import errors
- Verify all dependencies are installed:
uv sync
- Check Python version:
python --version
- Ensure you're using Python 3.13
- Verify all dependencies are installed:
- ⏳ Large document processing
- Reduce token limit for better performance
- Consider splitting large documents
- Monitor memory usage during OCR operations
🐛 Debug Information
To get more detailed error information, check the Claude Desktop logs or run the server manually in a PowerShell window.
📁 File Structure
📄 Supported Document Formats
- 📊 Microsoft Office: .docx, .xlsx, .pptx
- 📖 PDF: Regular PDFs and scanned PDFs (via OCR)
⚡ Performance Considerations
- 🔍 OCR Processing: Scanned documents take significantly longer to process
- 🎯 Token Limits: Adjust based on your document sizes and Claude's context window
- 💾 Memory Usage: Large documents and OCR operations can be memory-intensive
🤝 Contributing
When contributing to this project:
- Ensure compatibility with Windows and Python 3.13
- Test with various document formats
- Verify OCR functionality with scanned documents
- Update documentation for any new features
📚 Related Documentation
🗺️ Roadmap and Future Enhancements
🔮 Planned Features
- 🧠 Vector Storage and RAG Integration: Future versions will include vectorial document storage to:
- Reduce token consumption by avoiding repeated text extraction
- Enable semantic search across document collections
- Provide more efficient document retrieval and chunking
- Support for persistent document indexing
- 🔍 Enhanced OCR Validation: Currently, OCR functionality for scanned books has not been fully validated and may encounter issues with:
- Complex layouts and formatting
- Multi-column documents
- Poor quality scans
- Non-standard fonts or languages
💡 Current Recommendations
🚀 For Large Context Models
- 🤖 Gemini Models: With 1M+ token context windows, you can process very long documents without truncation
- 🎯 Token Management: Current implementation supports up to 128K tokens by default, but can be adjusted for larger context models
- 📖 Document Processing: Consider using higher token limits (e.g., 500K-1M) when working with:
- Complete books or long reports
- Multiple related documents
- Comprehensive document analysis
⚠️ Limitations to Consider
- 🔍 OCR Reliability: Scanned document processing is experimental and may require manual validation
- ⏳ Processing Time: Large documents and OCR operations can be time-intensive
- 💾 Memory Usage: High-resolution scanned documents may require significant system resources
This server cannot be installed
A Model Context Protocol server that allows AI assistants to discover, load, and process local documents on Windows systems, with support for multiple file formats and OCR capabilities for scanned PDFs.
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