puppeteer

The puppeteer-mcp-server exposes browser automation capabilities through a Model Context Protocol interface backed by a real headless browser via Puppeteer. This allows LLMs to perform actions such as navigating web pages, filling forms, executing JavaScript, taking screenshots, and retrieving page content in a real browser context. You can send model-context-based requests to interact with web pages as if you were operating a browser, enabling tasks like data extraction, automated testing, or content generation that depends on live web data. The server serves over HTTP on the configured port (default 8000 in the Docker run command), and is ready to receive MCP requests that leverage Puppeteer for browser automation and page interactions.

Prerequisites:

• Docker installed and running on your machine

Install / Run:

1. Build the Docker image (if you have a Dockerfile in your project): docker build -t puppeteer-mcp-server .

2. Run the container (exposes port 8000 for MCP communications): docker run --rm -p 8000:8000 puppeteer-mcp-server

Note:

• If you already have a prebuilt image named puppeteer-mcp-server, you can bypass the build step and run the container directly using the docker run command above.
• Ensure your environment allows network access for Puppeteer to launch a browser and for MCP request traffic to reach port 8000.

Tips and common considerations:

• The server runs a Puppeteer-backed browser; ensure the container has sufficient resources (memory/CPU) for headless browser operations.
• If you encounter missing dependencies in some environments, consider running with additional flags or using a variant image that includes necessary libraries for Puppeteer.
• Default port is 8000 as shown in the run command; adjust if you map ports differently.
• For production, you may want to configure security measures, logging, and request limits around the MCP interface.
• When debugging, inspect container logs to verify browser launch and MCP request handling.