If you're an SDET or a frontend engineer, you know the drill. You're sipping your morning coffee when a Slack alert pops up: "CI Build Failed: E2E Tests" . You open GitHub Actions, download the 150MB trace.zip artifact, run npx playwright show-trace , wait for the UI to load, click through the timeline, dig into the network tabs, and finally spot the 500 error or the missing DOM element. Then — because it's 2026 and we use AI for everything — you copy the error log, grab a snippet of the HTML, and paste it into Claude or ChatGPT: "Why did this fail?" It's tedious. It's manual. It's exactly the kind of repetitive work AI was supposed to eliminate. The problem? LLMs can't natively read a binary trace.zip . And even if you extract the raw JSON, it's massive — often exceeding the context window with useless static assets and bloated DOM dumps. This article walks through how I built an open-source MCP server that solves this. What is MCP?…