Penpot MCP Server

AI-powered design tool access for self-hosted Penpot via Model Context Protocol.

What is this?

An MCP server that gives AI agents (like Claude Code, Cursor, or any MCP-compatible client) full programmatic access to your self-hosted Penpot instance. AI can read, create, modify, and export design elements — from rectangles and text to full UI components — all through natural language.

Think of it as the bridge between your AI assistant and your design tool.

Problems it solves

Architecture

graph TB
    AI["AI Agent<br/>(Claude Code, Cursor, etc.)"]
    MCP["penpot-mcp<br/>Python + FastMCP<br/>:8787"]
    PG["PostgreSQL<br/>penpot-postgres<br/>:5432"]
    BE["Penpot Backend<br/>penpot-backend<br/>:6060"]
    FE["Penpot Frontend<br/>penpot-frontend<br/>:8080"]
    EX["Penpot Exporter<br/>penpot-exporter<br/>:6061"]

    AI -->|"Streamable HTTP"| MCP
    MCP -->|"Direct SQL reads<br/>(asyncpg)"| PG
    MCP -->|"RPC API writes<br/>(httpx)"| BE
    MCP -->|"Export requests"| BE
    BE -->|"Render"| EX
    FE -->|"Proxy"| BE

    style AI fill:#7c3aed,color:#fff
    style MCP fill:#2563eb,color:#fff
    style PG fill:#16a34a,color:#fff
    style BE fill:#ea580c,color:#fff
    style FE fill:#ea580c,color:#fff
    style EX fill:#ea580c,color:#fff

Dual-access strategy:

• Reads go directly to PostgreSQL via asyncpg — fast and reliable
• Writes go through Penpot's RPC API via httpx — ensures proper change tracking and undo history
• Exports use Penpot's built-in exporter (headless Chromium) for pixel-perfect SVG/PNG output

Tech Stack

Quick Start

Prerequisites

1. Self-hosted Penpot running via Docker Compose (official guide)
2. Docker and Docker Compose v2 installed
3. Access tokens enabled in your Penpot instance (see Enable Access Tokens)

Option A: Automated Setup

git clone https://github.com/ancrz/penpot-mcp-server.git
cd penpot-mcp-server
chmod +x setup.sh
./setup.sh

The script will guide you through configuration, build the Docker image, and start the server.

Option B: Manual Setup

1. Clone the repository

git clone https://github.com/ancrz/penpot-mcp-server.git
cd penpot-mcp-server

2. Create your configuration

cp .env.example .env

Edit .env with your Penpot details:

# Your Penpot access token (see "Enable Access Tokens" below)
PENPOT_ACCESS_TOKEN=your-token-here

# Your Penpot database password (from your Penpot docker-compose.yml)
PENPOT_DB_PASS=your-db-password

# Public URL where you access Penpot in the browser
PENPOT_PUBLIC_URL=http://localhost:9001

3. Add the MCP service to your Penpot Docker stack

Add the penpot-mcp service definition to your existing Penpot docker-compose.yml. See docker-compose.penpot.yml for the complete service definition to copy.

4. Build and start

docker compose up -d --build penpot-mcp

5. Verify it's running

curl -s http://localhost:8787/mcp \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-03-26","capabilities":{},"clientInfo":{"name":"test","version":"1.0"}}}'

You should see a JSON response with the server capabilities.

Connect Your AI Agent

Penpot MCP uses network transport (streamable HTTP) — the server runs as a Docker container and clients connect via HTTP. This means:

• The server is always running independently (via Docker Compose)
• The client only needs the URL to connect — no process spawning
• env in the client's JSON config is irrelevant — credentials live in the server's own .env file (configured during setup)
• Any client on the same machine (or network) can connect to http://localhost:8787/mcp

Key difference from stdio servers: With stdio servers (like Skill Swarm), the client launches the process and injects env vars. With network servers like Penpot MCP, the server manages its own credentials. The env block in your client's MCP config has no effect.

Claude Code

Claude Code uses "type": "http" for streamable HTTP connections.

Global (~/.claude.json):

{
  "mcpServers": {
    "penpot": {
      "type": "http",
      "url": "http://localhost:8787/mcp"
    }
  }
}

Project-level (.mcp.json in your project root):

{
  "mcpServers": {
    "penpot": {
      "type": "http",
      "url": "http://localhost:8787/mcp"
    }
  }
}

Restart Claude Code. You should see 66 tools from the penpot server listed when you run /mcp.

Note: Use "type": "http", not "streamable-http". Claude Code maps http to the streamable HTTP transport internally. Using streamable-http will cause a schema validation error.

Gemini CLI

Gemini CLI uses httpUrl (not url) for streamable HTTP connections. Transport is inferred from the field name.

Config file: ~/.gemini/settings.json

{
  "mcpServers": {
    "penpot": {
      "httpUrl": "http://localhost:8787/mcp"
    }
  }
}

Note: Gemini CLI distinguishes between url (SSE transport) and httpUrl (streamable HTTP transport). Penpot MCP uses streamable HTTP, so use httpUrl. No type field needed.

Antigravity

Antigravity uses serverUrl for HTTP-based MCP servers.

Config file: ~/.gemini/antigravity/mcp_config.json

{
  "mcpServers": {
    "penpot": {
      "serverUrl": "http://localhost:8787/mcp"
    }
  }
}

Note: Antigravity uses serverUrl (not url or httpUrl). If Antigravity runs inside Docker, make sure it can reach localhost:8787 on the host — you may need host.docker.internal:8787 instead of localhost:8787 depending on your Docker network setup.

Quick Comparison

Example prompts

Once connected, you can ask your AI agent things like:

• "List my Penpot projects"
• "Create a login form with email/password fields and a submit button"
• "Export the Login Card frame as SVG"
• "What colors are defined in the design system?"
• "Add a comment at position (100, 200) saying 'Review this layout'"

Tools Overview

The server provides 66 tools across 11 categories. See TOOLS.md for the complete reference with all parameters.

Configuration Reference

All settings are via environment variables. See .env.example for a template.

Enable Access Tokens

Penpot requires a feature flag to enable API access tokens.

1. Update your Penpot .env file

Add enable-access-tokens to your PENPOT_FLAGS:

PENPOT_FLAGS=enable-login-with-password enable-registration enable-access-tokens

2. Restart Penpot

docker compose restart penpot-backend penpot-frontend

3. Create a token

1. Open Penpot in your browser
2. Click your avatar (bottom-left) → Access Tokens
3. Click "Generate new token"
4. Give it a name (e.g., "MCP Server")
5. Copy the token and paste it into your .env as PENPOT_ACCESS_TOKEN

Penpot Docker Integration

The MCP server runs as a Docker container alongside your existing Penpot stack. You need to add it to your Penpot docker-compose.yml.

See docker-compose.penpot.yml for the exact service definition to add. The key points:

• It connects to the penpot Docker network (same as other Penpot services)
• It depends on penpot-postgres (with health check) and penpot-backend
• It exposes port 8787 on localhost only (127.0.0.1:8787:8787)
• Environment variables reference Docker internal hostnames

Development

Running locally (outside Docker)

# Install uv if needed
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install dependencies
uv sync

# Run the server (needs .env configured for local access)
uv run penpot-mcp

For local development, point PENPOT_DB_HOST and PENPOT_DB_PORT to your host-mapped PostgreSQL port, and PENPOT_BASE_URL to http://localhost:9001.

Running tests

uv sync --group dev
uv run pytest tests/ -v

Project structure

penpot-mcp-server/
├── src/penpot_mcp/
│   ├── server.py            # FastMCP entry point, 66 tool registrations
│   ├── config.py            # Pydantic Settings configuration
│   ├── services/
│   │   ├── db.py            # asyncpg connection pool
│   │   ├── api.py           # httpx RPC API client
│   │   ├── changes.py       # Penpot change operations builder
│   │   └── transit.py       # Transit+JSON decoder
│   ├── tools/
│   │   ├── projects.py      # Team & project queries
│   │   ├── files.py         # File CRUD operations
│   │   ├── shapes.py        # Shape reading & search
│   │   ├── create.py        # Shape creation
│   │   ├── modify.py        # Shape modification
│   │   ├── text.py          # Text operations
│   │   ├── export.py        # PNG/SVG export
│   │   ├── components.py    # Components & design tokens
│   │   ├── comments.py      # Comments & collaboration
│   │   ├── media.py         # Media assets & fonts
│   │   ├── database.py      # Raw SQL queries
│   │   └── advanced.py      # File raw data & revision comparison
│   └── transformers/
│       ├── css.py           # Shape → CSS conversion
│       ├── svg.py           # Shape → SVG conversion
│       └── layout.py        # Layout → CSS flexbox/grid
├── tests/
│   ├── conftest.py
│   ├── test_projects.py
│   ├── test_files.py
│   ├── test_shapes.py
│   └── test_e2e_login_form.py
├── pyproject.toml
├── Dockerfile
├── .env.example
├── setup.sh
├── docker-compose.penpot.yml
├── TOOLS.md
└── LICENSE

License

This project is licensed under the Apache License 2.0.

Acknowledgments

• Penpot — The open-source design platform
• Model Context Protocol — The protocol standard
• FastMCP — Python MCP SDK