# Penpot MCP Server 🎨🤖

Penpot MCP Logo

AI-Powered Design Workflow Automation
Connect Claude AI and other LLMs to Penpot designs via Model Context Protocol

--- ## 🚀 What is Penpot MCP? **Penpot MCP** is a revolutionary Model Context Protocol (MCP) server that bridges the gap between AI language models and [Penpot](https://penpot.app/), the open-source design and prototyping platform. This integration enables AI assistants like Claude (in both Claude Desktop and Cursor IDE) to understand, analyze, and interact with your design files programmatically. ### 🎯 Key Benefits - **🤖 AI-Native Design Analysis**: Let Claude AI analyze your UI/UX designs, provide feedback, and suggest improvements - **⚡ Automated Design Workflows**: Streamline repetitive design tasks with AI-powered automation - **🔍 Intelligent Design Search**: Find design components and patterns across your projects using natural language - **📊 Design System Management**: Automatically document and maintain design systems with AI assistance - **🎨 Cross-Platform Integration**: Works with any MCP-compatible AI assistant (Claude Desktop, Cursor IDE, etc.) ## 🎥 Demo Video Check out our demo video to see Penpot MCP in action: [![Penpot MCP Demo](https://img.youtube.com/vi/vOMEh-ONN1k/0.jpg)](https://www.youtube.com/watch?v=vOMEh-ONN1k) ## ✨ Features ### 🔌 Core Capabilities - **MCP Protocol Implementation**: Full compliance with Model Context Protocol standards - **Real-time Design Access**: Direct integration with Penpot's API for live design data - **Component Analysis**: AI-powered analysis of design components and layouts - **Export Automation**: Programmatic export of design assets in multiple formats - **Design Validation**: Automated design system compliance checking ### 🛠️ Developer Tools - **Command-line Utilities**: Powerful CLI tools for design file analysis and validation - **Python SDK**: Comprehensive Python library for custom integrations - **REST API**: HTTP endpoints for web application integration - **Extensible Architecture**: Plugin system for custom AI workflows ### 🎨 AI Integration Features - **Claude Desktop & Cursor Integration**: Native support for Claude AI assistant in both Claude Desktop and Cursor IDE - **Design Context Sharing**: Provide design context to AI models for better responses - **Visual Component Recognition**: AI can "see" and understand design components - **Natural Language Queries**: Ask questions about your designs in plain English - **IDE Integration**: Seamless integration with modern development environments ## 💡 Use Cases ### For Designers - **Design Review Automation**: Get instant AI feedback on accessibility, usability, and design principles - **Component Documentation**: Automatically generate documentation for design systems - **Design Consistency Checks**: Ensure brand guidelines compliance across projects - **Asset Organization**: AI-powered tagging and categorization of design components ### For Developers - **Design-to-Code Workflows**: Bridge the gap between design and development with AI assistance - **API Integration**: Programmatic access to design data for custom tools and workflows - **Automated Testing**: Generate visual regression tests from design specifications - **Design System Sync**: Keep design tokens and code components in sync ### For Product Teams - **Design Analytics**: Track design system adoption and component usage - **Collaboration Enhancement**: AI-powered design reviews and feedback collection - **Workflow Optimization**: Automate repetitive design operations and approvals - **Cross-tool Integration**: Connect Penpot with other tools in your design workflow ## 🚀 Quick Start ### Prerequisites - **Python 3.12+** (Latest Python recommended for optimal performance) - **Penpot Account** ([Sign up free](https://penpot.app/)) - **Claude Desktop or Cursor IDE** (Optional, for AI integration) ## Installation ### Prerequisites - Python 3.12+ - Penpot account credentials ### Installation #### Option 1: Install from PyPI ```bash pip install penpot-mcp ``` #### Option 2: Using uv (recommended for modern Python development) ```bash # Install directly with uvx (when published to PyPI) uvx penpot-mcp # For local development, use uvx with local path uvx --from . penpot-mcp # Or install in a project with uv uv add penpot-mcp ``` #### Option 3: Install from source ```bash # Clone the repository git clone https://github.com/montevive/penpot-mcp.git cd penpot-mcp # Using uv (recommended) uv sync uv run penpot-mcp # Or using traditional pip python -m venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate pip install -e . ``` ### Configuration Create a `.env` file based on `env.example` with your Penpot credentials: ``` PENPOT_API_URL=https://design.penpot.app/api PENPOT_USERNAME=your_penpot_username PENPOT_PASSWORD=your_penpot_password PORT=5000 DEBUG=true ``` > **⚠️ CloudFlare Protection Notice**: The Penpot cloud site (penpot.app) uses CloudFlare protection that may occasionally block API requests. If you encounter authentication errors or blocked requests: > 1. Open your web browser and navigate to [https://design.penpot.app](https://design.penpot.app) > 2. Log in to your Penpot account > 3. Complete any CloudFlare human verification challenges if prompted > 4. Once verified, the API requests should work normally for a period of time ## Usage ### Running the MCP Server ```bash # Using uvx (when published to PyPI) uvx penpot-mcp # Using uvx for local development uvx --from . penpot-mcp # Using uv in a project (recommended for local development) uv run penpot-mcp # Using the entry point (if installed) penpot-mcp # Or using the module directly python -m penpot_mcp.server.mcp_server ``` ### Debugging the MCP Server To debug the MCP server, you can: 1. Enable debug mode in your `.env` file by setting `DEBUG=true` 2. Use the Penpot API CLI for testing API operations: ```bash # Test API connection with debug output python -m penpot_mcp.api.penpot_api --debug list-projects # Get details for a specific project python -m penpot_mcp.api.penpot_api --debug get-project --id YOUR_PROJECT_ID # List files in a project python -m penpot_mcp.api.penpot_api --debug list-files --project-id YOUR_PROJECT_ID # Get file details python -m penpot_mcp.api.penpot_api --debug get-file --file-id YOUR_FILE_ID ``` ### Command-line Tools The package includes utility command-line tools: ```bash # Generate a tree visualization of a Penpot file penpot-tree path/to/penpot_file.json # Validate a Penpot file against the schema penpot-validate path/to/penpot_file.json ``` ### MCP Monitoring & Testing #### MCP CLI Monitor ```bash # Start your MCP server in one terminal python -m penpot_mcp.server.mcp_server # In another terminal, use mcp-cli to monitor and interact with your server python -m mcp.cli monitor python -m penpot_mcp.server.mcp_server # Or connect to an already running server on a specific port python -m mcp.cli monitor --port 5000 ``` #### MCP Inspector ```bash # Start your MCP server in one terminal python -m penpot_mcp.server.mcp_server # In another terminal, run the MCP Inspector (requires Node.js) npx @modelcontextprotocol/inspector ``` ### Using the Client ```bash # Run the example client penpot-client ``` ## MCP Resources & Tools ### Resources - `server://info` - Server status and information - `penpot://schema` - Penpot API schema as JSON - `penpot://tree-schema` - Penpot object tree schema as JSON - `rendered-component://{component_id}` - Rendered component images - `penpot://cached-files` - List of cached Penpot files ### Tools - `list_projects` - List all Penpot projects - `get_project_files` - Get files for a specific project - `get_file` - Retrieve a Penpot file by its ID and cache it - `export_object` - Export a Penpot object as an image - `get_object_tree` - Get the object tree structure for a Penpot object - `search_object` - Search for objects within a Penpot file by name ## AI Integration The Penpot MCP server can be integrated with AI assistants using the Model Context Protocol. It supports both Claude Desktop and Cursor IDE for seamless design workflow automation. ### Claude Desktop Integration For detailed Claude Desktop setup instructions, see [CLAUDE_INTEGRATION.md](CLAUDE_INTEGRATION.md). Add the following configuration to your Claude Desktop config file (`~/Library/Application Support/Claude/claude_desktop_config.json` on macOS or `%APPDATA%\Claude\claude_desktop_config.json` on Windows): ```json { "mcpServers": { "penpot": { "command": "uvx", "args": ["penpot-mcp"], "env": { "PENPOT_API_URL": "https://design.penpot.app/api", "PENPOT_USERNAME": "your_penpot_username", "PENPOT_PASSWORD": "your_penpot_password" } } } } ``` ### Cursor IDE Integration Cursor IDE supports MCP servers through its AI integration features. To configure Penpot MCP with Cursor: 1. **Install the MCP server** (if not already installed): ```bash pip install penpot-mcp ``` 2. **Configure Cursor settings** by adding the MCP server to your Cursor configuration. Open Cursor settings and add: ```json { "mcpServers": { "penpot": { "command": "uvx", "args": ["penpot-mcp"], "env": { "PENPOT_API_URL": "https://design.penpot.app/api", "PENPOT_USERNAME": "your_penpot_username", "PENPOT_PASSWORD": "your_penpot_password" } } } } ``` 3. **Alternative: Use environment variables** by creating a `.env` file in your project root: ```bash PENPOT_API_URL=https://design.penpot.app/api PENPOT_USERNAME=your_penpot_username PENPOT_PASSWORD=your_penpot_password ``` 4. **Start the MCP server** in your project: ```bash # In your project directory penpot-mcp ``` 5. **Use in Cursor**: Once configured, you can interact with your Penpot designs directly in Cursor by asking questions like: - "Show me all projects in my Penpot account" - "Analyze the design components in project X" - "Export the main button component as an image" - "What design patterns are used in this file?" ### Key Integration Features Both Claude Desktop and Cursor integration provide: - **Direct access** to Penpot projects and files - **Visual component analysis** with AI-powered insights - **Design export capabilities** for assets and components - **Natural language queries** about your design files - **Real-time design feedback** and suggestions - **Design system documentation** generation ## Package Structure ``` penpot_mcp/ ├── api/ # Penpot API client ├── server/ # MCP server implementation │ ├── mcp_server.py # Main MCP server │ └── client.py # Client implementation ├── tools/ # Utility tools │ ├── cli/ # Command-line interfaces │ └── penpot_tree.py # Penpot object tree visualization ├── resources/ # Resource files and schemas └── utils/ # Helper utilities ``` ## Development ### Testing The project uses pytest for testing: ```bash # Using uv (recommended) uv sync --extra dev uv run pytest # Run with coverage uv run pytest --cov=penpot_mcp tests/ # Using traditional pip pip install -e ".[dev]" pytest pytest --cov=penpot_mcp tests/ ``` ### Linting ```bash # Using uv (recommended) uv sync --extra dev # Set up pre-commit hooks uv run pre-commit install # Run linting uv run python lint.py # Auto-fix linting issues uv run python lint.py --autofix # Using traditional pip pip install -r requirements-dev.txt pre-commit install ./lint.py ./lint.py --autofix ``` ## Contributing Contributions are welcome! Please feel free to submit a Pull Request. 1. Fork the repository 2. Create your feature branch (`git checkout -b feature/amazing-feature`) 3. Commit your changes (`git commit -m 'Add some amazing feature'`) 4. Push to the branch (`git push origin feature/amazing-feature`) 5. Open a Pull Request Please make sure your code follows the project's coding standards and includes appropriate tests. ## License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ## Acknowledgments - [Penpot](https://penpot.app/) - The open-source design and prototyping platform - [Model Context Protocol](https://modelcontextprotocol.io) - The standardized protocol for AI model context

Penpot Mcp

Install

Penpot Mcp

Install