📚 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

Related MCP server: MCP File System Server

🛠️ Tools Available

• list_documents: Find documents by path, name, and extension

• load_documents: Extract document content as markdown

• load_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.

1. Download the latest Poppler Windows release from: https://github.com/oschwartz10612/poppler-windows/releases/

2. Extract the ZIP file to:

   D:\Program Files\poppler-24.08.0

3. The Poppler binaries should be located at:

   D:\Program Files\poppler-24.08.0\Library\bin

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.

1. Download Tesseract for Windows from: https://github.com/UB-Mannheim/tesseract/wiki

2. Install Tesseract following the installer instructions

3. 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 conversion

• mcp[cli]>=1.10.1 - MCP server framework

• opencv-python>=4.11.0.86 - Image processing

• pdf2image>=1.17.0 - PDF to image conversion

• pytesseract>=0.3.13 - Tesseract OCR wrapper

• python-dotenv>=1.1.1 - Environment variable management

• tiktoken>=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

• Second argument: Maximum tokens per document

  • Example: "30000"

  • Adjust based on your needs and Claude's token limits

📝 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

1. 📋 List Documents: Discover all documents in your configured directory

2. 📄 Load Standard Documents: Process Word docs, PDFs, PowerPoint, Excel files

3. 🔍 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

1. 🔍 Poppler not found

   • Verify Poppler installation path

   • Check .env file configuration

   • Ensure path uses double backslashes in Windows

2. 👁️ Tesseract not found

   • Verify Tesseract installation

   • Add Tesseract to system PATH

   • Restart command prompt/PowerShell

3. 🔐 Permission denied errors

   • Ensure the document directory is accessible

   • Check file permissions

   • Run as administrator if necessary

4. ❌ Import errors

   • Verify all dependencies are installed: uv sync

   • Check Python version: python --version

   • Ensure you're using Python 3.13

5. ⏳ 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:

1. Ensure compatibility with Windows and Python 3.13

2. Test with various document formats

3. Verify OCR functionality with scanned documents

4. Update documentation for any new features

📚 Related Documentation

• MCP Documentation

• Claude Desktop MCP Guide

• PDF2Image

• Poppler PDF Processing

• Tesseract OCR

• MarkItDown

🗺️ 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