DEV Community

Yet another engineer
Yet another engineer

Posted on

AI Controlled Browser That Worked

#automation #ai #browser #playwright

Playwright MCP

I like to be straightforward and head to the main point. Still, I believe it makes sense to share some background so people understand where I come from and would trust this is not just another clickbait.

I don’t think I am here to write yet another tutorial. I just want to share my honest experience so someone can explore this if relevant. Sharing relevant tips under comments would be appreciated 🤝

Introduction

To be honest, I have been hyped on browser automation and laptop automation for a while now—possibly several years.

During this period, I have made some automation for keyboard keystrokes, mouse moves, and image recognition. I had a cross-platform laptop app for that, and this was some sort of hobby project I even managed to use at work to go through some boring testing flows with repetitive behavior. As I started to see that most automation is focused on browsers, I began exploring there. I found out about Google Recorder, Playwright, etc. But the issue with all those is the time-consuming setup.

I was keeping my eye on the next big thing, and one day I noticed Playwright MCP was available to install.

Playwright MCP and Copilot Usage

I don’t think I am here to write yet another tutorial. I just want to share my honest positive experience, so not much about setup.

I tried it with VS Code and GitHub Copilot (available with Cursor as well). And this is pretty much it to get started, but let’s share some tips:

Tips for Using Playwright MCP

  1. Use Existing Browser Sessions: Run your web browser with parameters for remote control, such as: 
   -remote-debugging-port=9222 \ -user-data-dir="some path"
Enter fullscreen mode Exit fullscreen mode

This worked for Chromium-based browsers. Next, define it in mcp.json for Playwright MCP instructions if using Copilot.

  1. Build a Knowledge Base: At the time of writing, I believe the best way to work with AI is by building a knowledge base. For that, .md files are best. Start by putting brief instructions in chat to succeed with Agent delivering the task. Once you do, ask to build .md files with instructions and learnings so they can be reused in the future.

  2. Keep It Small: Ensure the context is not overdue. Another good reason to keep relevant context outside chat. I mostly ask to use a TEMP-named folder and put all learnings and progress there so I can initiate a new chat with all context easily and already summarized.

Enriched Insights and Tips on Playwright MCP

Leveraging Playwright MCP for Modern Test Automation

Playwright MCP (Model Context Protocol) has emerged as a game-changer in the realm of browser automation and testing. Articles like "Modern Test Automation with AI(LLM) and Playwright MCP" emphasize its ability to deliver high-quality applications at scale. The integration of AI tools like GitHub Copilot further enhances its capabilities, making it a preferred choice for developers.

Practical Tips for Using Playwright MCP

  1. Use Existing Browser Sessions: As highlighted, running your browser with parameters like -remote-debugging-port=9222 and -user-data-dir="some path" allows seamless integration with Playwright MCP. This approach minimizes setup time and leverages your daily browser environment.

  2. Build a Knowledge Base: Many experts recommend maintaining a .md file to document learnings and instructions. This not only aids in reusability but also ensures that your workflows are well-documented for future reference.

  3. Focus on Contextual Automation: Articles like "Generative Automation Testing with Playwright MCP Server" stress the importance of context-aware automation. By leveraging AI trends, you can create more robust and adaptive test scripts.

  4. Combine with Other Tools: Integrating Playwright MCP with tools like Semantic Kernel or Claude’s Model Context Protocol can unlock advanced capabilities, as seen in "Build an AI App That Can Browse the Internet Using Microsoft’s Playwright MCP Server & Semantic Kernel."

Community Insights

  • Engage with Comments: Many articles underline the value of community feedback. Engaging with comments under relevant articles can provide additional tips and real-world use cases. For instance, one user highlighted the challenge of running Playwright MCP in CI pipelines without tools like Cursor or Claude Desktop, sparking discussions on potential solutions.
  • API Compatibility: Another user inquired about integrating Playwright MCP with Gemini through APIs, showcasing the community's interest in expanding its interoperability.
  • General Feedback: Comments also include positive feedback, such as one user describing the insights as "insightful," which reinforces the value of shared knowledge.

Final Thoughts

Playwright MCP is not just a tool but a gateway to modernizing your automation workflows. By combining it with AI-driven tools and maintaining a structured knowledge base, you can significantly enhance your productivity and efficiency. Dive into the community, experiment with integrations, and let Playwright MCP redefine your approach to browser automation.

Top comments (0)