# Fullstory (ChatGPT App) > Product analytics for agents. - Type: app - Publisher: Fullstory (official) - Categories: Data & Analytics, AI & ML - Website: https://chatgpt.com/plugins/plugin_asdk_app_6a9a1ebb11fc81919ef2aa7231b9b068 - Open in ChatGPT: https://chatgpt.com/plugins/plugin_asdk_app_6a9a1ebb11fc81919ef2aa7231b9b068 - Version: 3.0.0 - Listed: 2026-10-04 ## About 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. ## How to use 1. **Add it in ChatGPT** Open Fullstory in ChatGPT's Plugin directory (button above), select + to add it and sign in if asked. 2. **Use it in a chat** Type @ in the composer and pick Fullstory, or start your message with its name. ChatGPT may also suggest it when it fits what you ask. ## Skills - **comparisons**: How to structure A vs B comparisons in Fullstory — when to use dimensionality (event/session properties) vs separate segments (user-level properties), and why the distinction matters for correctness. - **general-analysis**: Fullstory analytics workflow. Use when answering a question that requires measuring user behavior — counts, rates, trends, breakdowns, or cohort comparisons. Builds segments and metrics, computes results, then investigates sessions to explain what the numbers mean. - **session-review**: Use when diagnosing user-reported issues, investigating bugs, analyzing user behavior, or validating UI correctness using Fullstory session recordings. - **idea-generation**: Finds product improvement ideas a team has not already considered. Searches the metrics, segments and funnels the team already built, builds the ones that are missing, measures how many users each idea affects against a baseline, and pulls session replays as evidence. Returns 3 to 5 ranked proposals, each with what is new about it, a user count with its baseline, and replay links. Use when someone asks what to build next, how to improve a product area or flow, what the team is missing, for data-backed product ideas or proposals, or what changed after a release. If the user only wants the list of Fullstory Opportunities, call the Opportunities tools directly instead. For counts, rates and trends use general-analysis, for A vs B mechanics use comparisons, and for one session use session-review. ## Example prompts - "Find the page with the highest rage-click rate in the past 7 days. Pull 3 sessions and walk me through what happened." - "Build a segment of users who hit a console error this week, then pull two of their sessions and summarize what happened." - "Which users were highly active 60 days ago but haven't logged in for 30 days? Pull 5 sessions for their last week of activity." ## Related - [Higgsfield](https://chatgptplugins.directory/apps/higgsfield.md): Generate image and video ads, UGC videos and websites with many AI models - [Runway](https://chatgptplugins.directory/apps/runway.md): Generate and edit images, video and audio with AI models - [Datadog](https://chatgptplugins.directory/apps/datadog.md): Query and chart Datadog metrics, logs, traces and incidents - [Picsart](https://chatgptplugins.directory/apps/picsart.md): Generate and edit images, video and audio with Picsart AI models - [Mixpanel](https://chatgptplugins.directory/apps/mixpanel.md): Run segmentation, funnel and retention analysis on Mixpanel data - [Ahrefs](https://chatgptplugins.directory/apps/ahrefs.md): Keyword, backlink and competitor analysis, including AI search visibility --- Source: https://chatgptplugins.directory/apps/fullstory · Index: https://chatgptplugins.directory/llms.txt · Independent and community-run. Not affiliated with or endorsed by OpenAI. ChatGPT is a trademark of OpenAI.