About MCP-DevTools
MCP-DevTools is a Puppeteer-based MCP server for browser automation and DevTools access, aimed at Cursor users. It runs in Docker and includes non-root execution and Chromium sandboxing.
Key features
- 01Puppeteer browser automation
- 02DevTools Protocol integration
- 03Docker-based setup
- 04Non-root execution
- 05Chromium sandboxing
Use cases
- 01Automating browser tasks in Cursor
- 02Inspecting browser behavior through DevTools
- 03Running browser automation in a Docker environment
MCP support
It exposes Puppeteer browser automation and DevTools capabilities to MCP clients.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEMCP-DevTools
A Model Context Protocol (MCP) server implementation for enhanced browser automation and DevTools capabilities using Puppeteer.
Features
- Full browser automation support via Puppeteer
- Enhanced DevTools Protocol integration
- Docker support for consistent environments
- Non-root user execution for security
- Cross-platform compatibility
Prerequisites
- Docker
- Cursor IDE
Setup
- Build the Docker image:
docker build -t mcp/puppeteer .
- Configure in Cursor:
- Open Cursor Settings
- Navigate to MCP Configuration
- Add the following command:
docker run -i --rm --init -e DOCKER_CONTAINER=true mcp/puppeteer
Configuration
The server is configured to run on port 3000 by default. Environment variables:
PUPPETEER_EXECUTABLE_PATH: Path to Chromium executableDISPLAY: X11 display for headless modeCHROME_BIN: Chrome binary path
Security
- Runs as non-root user in Docker
- Uses Chromium's sandbox
- Implements proper permission handling
License
MIT License
Contributing
- Fork the repository
- Create your feature branch
- Commit your changes
- Push to the branch
- Create a new Pull Request
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.