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Introduction

We've all been there: repeating the same multi-step process in a browser over and over. Maybe you're filling out similar forms, checking multiple dashboards, or gathering data from different websites. These tasks are perfect for automation, but writing traditional automation scripts can be time-consuming and brittle.

What if you could describe these tasks in plain English instead? "Fill out this contact form with our company details," or "Navigate to the GitHub issues page and check for any bugs labeled 'critical'." AI agents can now understand these instructions and translate them into browser actions.

An AI Agent is powered by a large language model (LLM) that can parse your natural language instructions and figure out the steps needed to complete a task. But to actually interact with websites, the agent needs tools to control the browser.

Model Context Protocol (MCP)​

For an agent to interact with software, it needs a way to communicate. The Model Context Protocol (MCP) is that communication standard, think of it as a universal remote control for AI. It defines a common language that allows agents to understand and control different tools.

  • An MCP Client is the application where you interact with the agent. This could be your code editor (like VS Code with GitHub Copilot), a dedicated chat application, or a command-line interface.
  • An MCP Server exposes a set of tools that the agent can use. It's the "hands" that carry out the agent's instructions.

Playwright MCP?​

The Playwright MCP is a powerful MCP server that gives an AI agent control over a web browser. It's built on top of Playwright, the same technology that powers our reliable testing and automation framework.

Its key advantage is how it "sees" a web page. Instead of looking at pixels on a screen, it uses Playwright's accessibility snapshot. This is a structured representation of the page content, similar to what a screen reader uses. This makes it:

  • Fast and Lightweight: It doesn't need slow, expensive vision models to understand the page.
  • Reliable and Deterministic: It interacts with elements based on their role and name, not their position, which is far less brittle than screenshot-based approaches.
  • LLM-Friendly: It provides clean, structured data that language models can easily understand and act upon.

What Can I Use It For?​

The Playwright MCP is a versatile tool that's useful for both developers and anyone looking to automate their web-based workflows.

  • For Development:

    • Generate Tests: Ask an agent to write Playwright tests for you. "Generate a test that adds an item to the shopping cart and checks out."
    • Explore Your Website: Use the agent to navigate your website and understand its structure and propose possible test cases.

  • For Automation:

    • Automate Repetitive Tasks: Fill out forms, download reports, or perform any other multi-step process on a website.
    • Data Extraction: Extract information from web pages and summarize it.

Getting Started​

The quickest way to get started is to install the Playwright MCP server for your favorite MCP client. For many clients, like VS Code, this is a simple, one-click process.

Install in VS Code

This will create a new MCP server configuration in a JSON file in your VS Code settings.

{
  "mcpServers": {
    "playwright": {
      "command": "npx",
      "args": [
        "@playwright/mcp@latest"
      ]
    }
  }
}

Once installed, you can start giving the agent commands to control your browser right away.

For more installation options and configuration details, refer to the guides on the sidebar, or see the README.

What's Next?​

Now that you understand the basics, dive into our other guides to see what you can build: