Lizeur - PDF Content Extraction MCP Server

Lizeur is a Model Context Protocol (MCP) server that enables AI assistants to extract and read content from PDF documents using Mistral AI's OCR capabilities. It provides a simple interface for converting PDF files to markdown text that can be easily consumed by AI models.

<a href="https://glama.ai/mcp/servers/@SilverBzH/lizeur"> <img width="380" height="200" src="https://glama.ai/mcp/servers/@SilverBzH/lizeur/badge" alt="Lizeur MCP server" /> </a>

Features

• PDF OCR Processing: Uses Mistral AI's latest OCR model to extract text from PDF documents
• Intelligent Caching: Automatically caches processed documents to avoid re-processing
• Markdown Output: Returns clean markdown text for easy integration with AI workflows
• FastMCP Integration: Built with FastMCP for optimal performance and ease of use

Prerequisites

• Python 3.10
• UV package manager
• Mistral AI API key

Installation

From pypi

pip install lizeur

And add the following configuration to your mcp.json file:

Note: Lizeur will be installed in the python3.10 folder. If this folder is not in your system PATH, your IDE may not be able to detect the lizeur binary.

Solution: You can add the full path to the lizeur binary in the command field to ensure your IDE can locate it.

{
  "mcpServers": {
    "lizeur": {
      "command": "lizeur",
      "env": {
        "MISTRAL_API_KEY": "your-mistral-api-key-here",
        "CACHE_PATH": "your cache path",
      }
    }
  }
}

Manual

1. Clone the Repository

git clone https://github.com/SilverBzH/lizeur
cd lizeur

2. Create and Activate Virtual Environment

# Create a virtual environment
uv venv --python 3.10

# Activate the virtual environment
# On macOS/Linux:
source .venv/bin/activate

# On Windows:
# .venv\Scripts\activate

3. Install Dependencies and Build

# Install dependencies
uv sync

# Build the package
uv build

4. Install System-Wide

# Install the package system-wide
uv pip install --system .

This will install the lizeur command globally on your system.

Usage

Once configured, the MCP server provides two tools that can be used by AI assistants:

Available Functions

read_pdf

• Function: read_pdf
• Parameter: absolute_path (string) - The absolute path to the PDF file
• Returns: Complete OCR response including all pages with markdown content, bounding boxes, and other OCR metadata

read_pdf_text

• Function: read_pdf_text
• Parameter: absolute_path (string) - The absolute path to the PDF file
• Returns: Markdown text content from all pages without the full OCR metadata (simpler for agents to process)

Example Usage in AI Assistant

The AI assistant can now use the tools like this:

What the OP command looks like for this specific controller, here is the doc /path/to/document.pdf

The MCP server will:

1. Check if the document is already cached
2. If not cached, upload the PDF to Mistral AI for OCR processing This will use your MISTRAL API key and cost money
3. Extract the text and convert it to markdown
4. Cache the result for future use
5. Return the markdown content

Note: Use read_pdf_text when you only need the text content, or read_pdf when you need the complete OCR response with metadata. read_pdf can be confusion for some agent if the pdf file is big.

Development

Local Development Setup

# Install in development mode
uv pip install -e .

# Run the server directly
python main.py

Project Structure

• main.py - Main server implementation with FastMCP integration
• pyproject.toml - Project configuration and dependencies
• uv.lock - Locked dependency versions

Dependencies

• mcp[cli]>=1.12.4 - Model Context Protocol implementation
• mistralai>=0.0.10 - Mistral AI Python client

License

This project is licensed under the MIT License.

Support

For issues and questions, please refer to the project repository or contact the maintainers.