Creating a Playwright Automation Script Using Cursor And MCP
AI Summary
Summary of Video: MCP Server Experimentation
- Introduction to MCP:
- Model Context Protocol (MCP) serves as a bridge between LLM agents and various tools/services.
- File System MCP Server:
- Allows the agent to perform file operations: read, write, edit, and create directories.
- Playwright MCP:
- Enables browser automation tasks such as navigating websites, typing, and taking screenshots.
- Demonstrated by searching for the “Web Automation Playwright” book on Bington Books.
- Setting Up MCP Server:
- Requires NodeJS installation.
- Access settings in
cursor, add a new MCP server.- Information can be found on GitHub (Playwright MCP server and file system).
- Website Interaction:
- Goals: Add book to cart, choose New York as the state, and calculate shipping costs.
- Agent operates the browser interactively, displaying actions.
- Playwright Test Script Generation:
- Agent generates a Playwright test script based on the workflow.
- Emphasis on following best practices, including using modern locators and page object model.
- Project Creation:
- Automation project created with necessary structure:
package.json, configuration for Chrome, and README.- Test script verification includes checking title and shipping options.
- Debugging:
- Initial issues with locator accuracy highlighted during testing.
- Suggestions on how to address errors in script, reinforcing the importance of debug processes.
- Conclusion:
- Insights into MCP and AI interaction.
- Encouragement to experiment with the resources provided.