PDF MCP Server

An MCP server that enables reading PDF file contents, allowing PDF documents to be used as a knowledge base for LLMs.

Features

• High-Quality Extraction: Uses marker-pdf (via a Python backend) to extract text with layout awareness and high-fidelity LaTeX equation recognition.
• Robust Fallback: Automatically switches to a Node.js-based parser (pdf-parse) if the Python environment is unavailable or fails, ensuring extraction always succeeds (albeit with lower formatting quality).
• Smart Filtering: Supports page range extraction to process only relevant sections of large documents.

Installation

Prerequisites

• Node.js (v18+)
• Python (v3.10+) and pip (for high-quality extraction)

Setup

1. Install Node.js dependencies:

   npm install
   

2. Install Python dependencies (Recommended): To enable high-quality extraction (especially for scientific papers with math), install the Python dependencies.

   # Create or activate a virtual environment if desired
   python3 -m pip install -r python/requirements.txt
   

   Note: The first time you run the tool with the Python backend, it will download necessary AI models (OCR, layout analysis, etc.) to a local cache. This download is approximately 3.3GB. Ensure you have a stable internet connection.

3. Build the server:

   npm run build
   

Usage

Configuration for Claude/MCP Clients

Add this to your MCP settings configuration:

{
  "mcpServers": {
    "pdf-reader": {
      "command": "node",
      "args": ["/absolute/path/to/mcpPdf/dist/index.js"],
      "env": {
         // Optional: Override where python is found if not in venv or path
         // "PYTHON_PATH": "/path/to/python" 
      }
    }
  }
}

Tool: read_pdf

Reads and extracts text content from a PDF file.

Inputs:

• path (string): Absolute path to the PDF file.
• start_page (number, optional): Starting page number (1-based).
• end_page (number, optional): Ending page number (1-based).

How it works:

1. Attempt 1 (Python/Marker): The server tries to run the internal convert.py script.
   • If successfully configured, this loads the marker models from the local cache (.cache directory in the project).
   • It accurately converts equations to LaTeX and preserves document structure.
2. Attempt 2 (Fallback): If the Python script fails (e.g., missing dependencies, runtime error), the server catches the error and uses pdf-parse (a native Node.js library).
   • This extracts raw text. Equations may appear as linearized text, and layout may be less preserved.

Troubleshooting

• Permission Errors: The project is configured to use a local .cache directory for models to avoid system permission issues. If you encounter errors, ensure the project directory is writable.
• Slow Performance: The high-quality extraction uses deep learning models. It can be slow on large documents without a GPU. Use the start_page and end_page arguments to extract only what you need.