With Playwright MCP, AI-powered debugging goes way beyond just spotting errors. It can actually predict where things might break before they do, suggest automatic fixes for common issues, and even help optimize your test suites by looking at past test runs and figuring out patterns. The best part is that these insights can scale across large, complex enterprise applications, making your testing process much more efficient and less of a headache.
From my experience, keeping AI-generated code understandable is all about clear communication, both for yourself and anyone else who might work with it. I make it a habit to add inline comments wherever the AI makes decisions, so it’s obvious why a certain line or function exists. I also document any AI-driven choices separately, which helps when someone needs to review or audit the logic later. On top of that, I always keep the original scripts alongside the modified ones. Using version control and doing regular code reviews really helps maintain transparency and makes it way easier for human testers to jump in, understand what’s happening, and make safe changes when needed.
Absolutely! You can test Electron.js desktop apps with Playwright, but it takes a little extra setup. Think of it this way: MCP treats Electron windows kind of like browser tabs, so a lot of the usual interactions, like clicking buttons or filling forms, work pretty much the same. The catch is, some Electron-specific events or features might need a bit of custom handling. Once you tweak for those, it’s smooth sailing!
Absolutely! MCP can handle API testing too. You can automate your API calls, check the responses, and even get AI-driven suggestions for testing different endpoints. That said, it’s worth noting that MCP really shines when it comes to UI automation, so while API testing is supported, UI workflows are where it performs best.
Absolutely! MCP can actually generate Cucumber scenarios straight from your user stories or requirements, which is super handy. That said, I always double-check the generated steps against the real behavior of the app, just to make sure everything works as expected and nothing gets lost in translation. It saves a lot of time, but a little validation goes a long way.
Honestly, it really depends on how you’re using it. From my experience, you can save quite a bit on token usage if you’re smart about it. For example, batching your API calls instead of sending lots of tiny requests, caching results so you don’t repeat work, and reusing context wherever possible, all of these can make a noticeable difference without slowing things down. It’s less about MCP magically saving tokens and more about using it efficiently.
To be fair, AI could probably handle writing, running, and even debugging browser automation scripts on its own, but in real-world enterprise settings, we still really need a human in the loop. There are always tricky edge cases, compliance rules, or ambiguous situations where AI might make the wrong call. In controlled environments, full autonomy could work, but for now, I’d never skip a human review, it’s just too risky otherwise.