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Penpot MCP Server

by montevive
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Python
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Penpot MCP Server 🎨🤖

🚀 What is Penpot MCP?

Penpot MCP is a revolutionary Model Context Protocol (MCP) server that bridges the gap between AI language models and Penpot, 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

✨ 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)

  • Claude Desktop or Cursor IDE (Optional, for AI integration)

Installation

Prerequisites

  • Python 3.12+

  • Penpot account credentials

Installation

Option 1: Install from PyPI

pip install penpot-mcp

Option 2: Using uv (recommended for modern Python development)

# 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

# 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

Usage

Running the MCP Server

# 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:

# 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:

# 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

# 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

# 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

# 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.

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):

{ "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):

    pip install penpot-mcp

  2. Configure Cursor settings by adding the MCP server to your Cursor configuration. Open Cursor settings and add:

    { "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:

    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:

    # 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:

# 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

# 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 file for details.

Acknowledgments

Deploy Server
A
security – no known vulnerabilities
F
license - not found
A
quality - confirmed to work

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Tools

View all tools

Bridges AI language models with Penpot design platform, enabling AI assistants to analyze, search, and interact with design files programmatically for automated design workflows.

  1. 🚀 What is Penpot MCP?
    1. 🎯 Key Benefits
  2. 🎥 Demo Video
    1. ✨ Features
      1. 🔌 Core Capabilities
      2. 🛠️ Developer Tools
      3. 🎨 AI Integration Features
    2. 💡 Use Cases
      1. For Designers
      2. For Developers
      3. For Product Teams
    3. 🚀 Quick Start
      1. Prerequisites
    4. Installation
      1. Prerequisites
      2. Installation
      3. Configuration
    5. Usage
      1. Running the MCP Server
      2. Debugging the MCP Server
      3. Command-line Tools
      4. MCP Monitoring & Testing
      5. Using the Client
    6. MCP Resources & Tools
      1. Resources
      2. Tools
    7. AI Integration
      1. Claude Desktop Integration
      2. Cursor IDE Integration
      3. Key Integration Features
    8. Package Structure
      1. Development
        1. Testing
        2. Linting
      2. Contributing
        1. License
          1. Acknowledgments

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