MCP PDF Reader

MCP PDF Reader Server (Python + FastMCP)

A powerful Model Context Protocol (MCP) server built with FastMCP that provides comprehensive PDF processing capabilities including text extraction, image extraction, and OCR for reading text within images.

- Text Extraction: Extract text content from PDF pages
- Image Extraction: Extract all images from PDF files
- OCR Capabilities: Read text from images using Tesseract OCR
- Comprehensive Analysis: Get detailed PDF structure and metadata
- Page Range Support: Process specific page ranges
- Multiple Languages: OCR support for multiple languages

You need to install Tesseract OCR on your system:

sudo apt update sudo apt install tesseract-ocr tesseract-ocr-eng

- Download from:https://github.com/UB-Mannheim/tesseract/wiki
- Install and add to PATH
- Or use:conda install -c conda-forge tesseract

# For multiple languages sudo apt install tesseract-ocr-fra tesseract-ocr-deu tesseract-ocr-spa

# macOS/Linux curl -LsSf https://astral.sh/uv/install.sh | sh # Windows powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

mkdir mcp-pdf-reader-server cd mcp-pdf-reader-server

# Copy the files (pdf_reader_server.py and pyproject.toml) # Then install dependencies uv sync

uv run python -c "import pytesseract; print(pytesseract.get_tesseract_version())"

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

pip install fastmcp PyMuPDF pytesseract Pillow

{ "file_path": "/path/to/document.pdf", "page_range": {"start": 1, "end": 5} }

{ "file_path": "/path/to/document.pdf", "output_dir": "/path/to/images/", "page_range": {"start": 1, "end": 3} }

{ "file_path": "/path/to/document.pdf", "ocr_language": "eng+fra", "page_range": {"start": 1, "end": 10} }

{ "mcpServers": { "pdf-reader": { "command": "uv", "args": ["run", "python", "/path/to/your/pdf_reader_server.py"], "cwd": "/path/to/your/mcp-pdf-reader-server" } } }

{ "mcpServers": { "pdf-reader": { "command": "/path/to/your/.venv/bin/python", "args": ["/path/to/your/pdf_reader_server.py"] } } }

{ "mcpServers": { "pdf-reader": { "command": "python", "args": ["/path/to/your/pdf_reader_server.py"], "env": { "PYTHONPATH": "/path/to/your/.venv/lib/python3.x/site-packages" } } } }

{ "success": true, "file_path": "/path/to/document.pdf", "pages_processed": "1-3", "total_pages": 10, "pages_text": [ { "page_number": 1, "text": "Page 1 content...", "word_count": 125 } ], "combined_text": "All text combined...", "total_word_count": 1250, "total_character_count": 8750 }

{ "success": true, "file_path": "/path/to/document.pdf", "pages_processed": "1-2", "ocr_language": "eng", "pages_data": [ { "page_number": 1, "text": "Regular text from PDF...", "ocr_text": "Text extracted from images...", "images_with_text": [ { "image_index": 1, "ocr_text": "Text from image 1", "confidence": "high" } ], "combined_text": "Combined text and OCR...", "text_word_count": 100, "ocr_word_count": 25 } ], "summary": { "total_text_word_count": 200, "total_ocr_word_count": 50, "combined_word_count": 250, "images_processed": 3 }, "all_text_combined": "All extracted text..." }

- OCR processing can be slow for large images
- Consider processing smaller page ranges for faster results
- Images smaller than 50x50 pixels are automatically skipped

- Large PDFs with many images may consume significant memory
- The server processes pages sequentially to manage memory usage
- Extracted images are saved to disk to reduce memory pressure
- Use page rangesfor large documents
- Specify output directoriesfor image extraction to avoid temp file buildup
- Choose appropriate OCR languagesto improve accuracy and speed
- Preprocess imagesif OCR quality is poor (consider adding OpenCV)

TesseractNotFoundError: tesseract is not installed

- Install Tesseract OCR system package
- Ensure it's in your PATH

- Ensure the Python process has read access to PDF files
- Ensure write access to output directories

- Try different OCR language codes
- Consider image preprocessing
- Check if images are high enough resolution

- Process smaller page ranges
- Close other applications
- Consider increasing available RAM

PYTHONUNBUFFERED=1 uv run python pdf_reader_server.py

PYTHONUNBUFFERED=1 python pdf_reader_server.py

tesseract --list-langs tesseract image.png output.txt

- fastmcp: Modern MCP server framework
- PyMuPDF: Fast PDF processing and rendering
- pytesseract: Python wrapper for Tesseract OCR
- Pillow: Image processing library
- tesseract-ocr: System OCR engine

You can modify the OCR configuration in the code:

ocr_text = pytesseract.image_to_string( pil_image, lang=ocr_language, config='--psm 6 -c tessedit_char_whitelist=0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz ' )

For better OCR results, consider adding image preprocessing:

# Add to requirements: opencv-python, numpy import cv2 import numpy as np # Preprocessing example def preprocess_image(image): gray = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2GRAY) thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1] return Image.fromarray(thresh)

- Fork the repository
- Create a feature branch
- Add tests for new functionality
- Submit a pull request

MIT License - see LICENSE file for details.

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