Lizeur

# 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. ```json { "mcpServers": { "lizeur": { "command": "lizeur", "env": { "MISTRAL_API_KEY": "your-mistral-api-key-here", "CACHE_PATH": "your cache path", } } } } ``` ### Manual #### 1. Clone the Repository ```bash git clone https://github.com/SilverBzH/lizeur cd lizeur ``` #### 2. Create and Activate Virtual Environment ```bash # 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 ```bash # Install dependencies uv sync # Build the package uv build ``` #### 4. Install System-Wide ```bash # 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 ```bash # 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.