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.

Features

• 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

Prerequisites

System Dependencies

Tesseract OCR

You need to install Tesseract OCR on your system:

Ubuntu/Debian:

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

macOS:

brew install tesseract

Windows:

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

Additional Language Packs (Optional)

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

Installation

Quick Start with UV

1. Install UV (if not already installed):

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

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

2. Clone/Create the project:

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

3. Initialize and install with UV:

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

4. Verify installation:

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

Alternative: Manual Setup

If you prefer traditional setup:

1. Create virtual environment:

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

2. Install dependencies:

pip install fastmcp PyMuPDF pytesseract Pillow

Usage

Running the Server

With UV:

uv run python pdf_reader_server.py

Or if you have the environment activated:

python pdf_reader_server.py

The server will start and listen for MCP requests on stdin/stdout.

Available Tools

1. read_pdf_text

Extract text content from PDF pages.

Parameters:

• file_path (string, required): Path to the PDF file
• page_range (object, optional): Dict with start and end page numbers

Example:

*Configuration content*

2. extract_pdf_images

Extract all images from a PDF file.

Parameters:

• file_path (string, required): Path to the PDF file
• output_dir (string, optional): Directory to save images
• page_range (object, optional): Page range to process

Example:

*Configuration content*

3. read_pdf_with_ocr

Extract text from both regular text and images using OCR.

Parameters:

• file_path (string, required): Path to the PDF file
• page_range (object, optional): Page range to process
• ocr_language (string, optional): OCR language code (default: "eng")

Example:

*Configuration content*

Supported OCR Languages:

• eng - English
• fra - French
• deu - German
• spa - Spanish
• eng+fra - Multiple languages

4. get_pdf_info

Get comprehensive metadata and statistics about a PDF.

Parameters:

• file_path (string, required): Path to the PDF file

5. analyze_pdf_structure

Analyze the structure and content distribution of a PDF.

Parameters:

• file_path (string, required): Path to the PDF file

Configuration with Claude Desktop

With UV

Add this to your claude_desktop_config.json:

*Configuration content*

With Virtual Environment

*Configuration content*

System Python

*Configuration content*

Example Responses

Text Extraction Response

*Configuration content*

OCR Response

*Configuration content*

Performance Considerations

OCR Performance

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

Memory Usage

• 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

Optimization Tips

1. Use page ranges for large documents
2. Specify output directories for image extraction to avoid temp file buildup
3. Choose appropriate OCR languages to improve accuracy and speed
4. Preprocess images if OCR quality is poor (consider adding OpenCV)

Troubleshooting

Common Issues

1. Tesseract not found:

   TesseractNotFoundError: tesseract is not installed
   

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

2. Permission errors:

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

3. Poor OCR results:

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

4. Memory errors:

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

Debug Mode

Run with debug logging using UV:

PYTHONUNBUFFERED=1 uv run python pdf_reader_server.py

Or with regular Python:

PYTHONUNBUFFERED=1 python pdf_reader_server.py

Testing OCR

Test Tesseract directly:

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

Dependencies

• 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

Advanced Features

Custom OCR Configuration

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 '
)

Image Preprocessing

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)

Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Submit a pull request

License

MIT License - see LICENSE file for details.