Beyond test creation and debugging, what advanced AI-driven capabilities or functionalities does Playwright MCP offer for optimizing browser automation?
Can we pass requirements as additional context? Field names and rules, or Figma design? Or should we rely on AI to build a basic navigation and amend assertions according to the requirements (i.e. not what actually is built)
Does the MCP provide support for desctop app ?
How can I ask CoPilot to let me review the first solution of multiple solutions? It often will make 2 or 3 passes with “better solutions” when the first approach might have been acceptable.
Will it work for dynamic elements? where locators keep changing?
How can developers most effectively use the feedback loops between AI-driven debugging insights and the Playwright automation scripts?
Does anyone have similar experience with LLM tools in JetBrains IDEs?
How can AI-powered debugging in Playwright MCP be extended beyond error detection to provide predictive insights, automated fixes, and enterprise-scale optimization for complex web applications?
How do you maintain transparency in AI-generated code so that human testers can understand, modify, or audit it?
Can Playwright test Electron,js desktop apps the same way you just demonstrated with web?
Does the MCP provide support for API testing?
Can the MCP generate Cucumber for Playwright?
Considering the AI prices, does MCP save on token usage?
Could AI agents eventually write, run, and debug browser automation scripts on their own, without human input?