FullstoryFind the page with the highest rage-click rate in the past 7 days. Pull 3 sessions and walk me through what happened.FullstoryBuild a segment of users who hit a console error this week, then pull two of their sessions and summarize what happened.FullstoryWhich users were highly active 60 days ago but haven't logged in for 30 days? Pull 5 sessions for their last week of activity.

Ask about your product analytics, and get answers you can verify. Fullstory MCP connects Codex and ChatGPT to your product's behavioral data. Ask how many users adopted a feature, where checkout is leaking, or whether yesterday's release broke something, and Codex and ChatGPT build the segment, computes the metric, and explains the result with a link back to Fullstory. Analytics connectors can give you numbers. Fullstory shows you what happened. Metrics unpack into real session replay, so "conversion dropped 12%" becomes "the coupon field errors for logged-out users." And because Fullstory autocaptures clicks, page views, errors, and frustration signals, many questions need no instrumentation at all. Ask about rage clicks on pricing or errors in checkout without shipping a single event. What Codex and ChatGPT can do: (1) Build and save segments and metrics. Describe the users or behavior you care about in your own words; Codex and ChatGPT create segments or metrics in Fullstory where your whole team can reuse it. (2) Compute on demand. Counts, rates, trends, and breakdowns by device, browser, geography, or user property, over any time window. (3) Speak your product's language. Codex and ChatGPT discover your pages, named elements, and defined events, so questions map to your real product vocabulary instead of guessed selectors. (4) Analyze conversion. Read your saved funnels and measure where users drop and why. (5) Back every number with sessions. Retrieve the sessions and events behind any metric, including frustration signals like rage clicks and dead clicks. (6) Read sessions at scale. Ask Codex and ChatGPT to review dozens of sessions behind a drop-off or an alert and report the patterns, including failures that never reached your error tracker. Teams use it to let PMs, support, and engineers self-serve answers that used to queue behind an analyst, and to settle "why did this number move?" with evidence instead of theories.

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