Userback is where your user feedback lives. You collect it, manage it, triage it, and act on it, all in one place. Userback MCP extends that by connecting your Userback data to the AI tools you already use, like Claude or ChatGPT, so you can query and work with your feedback through conversation.
This article explains what Userback MCP does and how to use it, without any technical knowledge required. For setup instructions, see the Userback MCP documentation.
The Userback MCP works with Claude Desktop, Claude Code, ChatGPT, Cursor, VS Code, and Windsurf.
What Is Userback MCP?
MCP (Model Context Protocol) is an open standard that lets AI assistants connect to external tools. When Userback MCP is connected, your AI assistant can read and update your Userback data, live, in real time, as part of a normal conversation.
In practical terms: instead of opening Userback, filtering feedback, copying content, and pasting it into an AI tool, you just ask your question. Your AI goes and gets what it needs from Userback and gives you the answer you need.
One-time setup required: Connecting Userback MCP to your AI tool takes a few minutes and can be done by anyone. After that, you use it like a normal conversation.
📖 Setup guide: docs.userback.io/docs/userback-mcp
Five Things to Get You Started With Userback MCP
Nothing about your Userback workflow changes, MCP simply lets you reach your feedback from inside the AI tools you already use. Here are five ways to try it today!
1. Turn a backlog of feedback into a prioritized work plan
You've got a sprint planning session coming up and you know the themes are in Userback, but pulling them together, estimating impact, and shaping them into actual work packages takes time. Ask your AI assistant to do the analysis for you. It reads directly from Userback, clusters what's related, and surfaces a prioritized view you can act on quickly.
Try this prompt: Analyze all In Progress feedback in the [Project] project in Userback, cluster recurring themes, estimate impact, and propose an efficient implementation plan that minimizes time spent.
2. Check whether a feature you shipped actually landed
You released a fix or a new feature two weeks ago. You think it went well, support tickets feel quieter, but you want to know whether the feedback in Userback actually backs that up. Has the volume of complaints about that area dropped? Are users still raising the same issues, or has the conversation moved on? Ask your AI assistant to compare the picture.
Try this prompt: Compare feedback in [Project] related to [feature or area] from the 30 days before [release date] and the 30 days after. Has the volume changed? Are the same issues still coming up, or have new ones emerged?
3. Turn design feedback into an implementation plan
Users have been leaving feedback about a flow that isn't working well, you can see it in Userback. But getting from "users are struggling with checkout" to a concrete brief your developer can build from usually means synthesizing multiple feedback items, identifying the UX changes needed, and writing up acceptance criteria for QA. Ask your AI assistant to do that work directly from the Userback data, so you spend your time reviewing and refining a plan rather than building it from scratch.
Try this prompt: Build an implementation plan for any requested UX changes in the [Project] project and include acceptance criteria for QA.
4. Get on top of an escalation fast
A customer has gone to your CEO with a complaint and your CEO has forwarded it to you asking for context. You know this user has submitted feedback before, it's all in Userback, but finding quickly understanding the timeline, and pulling together a coherent picture under pressure takes time you don't have. Ask your AI assistant to surface everything related to that user or issue so you can respond with the full picture, not just what you can remember off the top of your head.
Try this prompt: Find all feedback in [Project] related to [user name or issue] and summarize the history, what was raised, when, what the current status is, and whether anything is still unresolved.
5. Get a new team member up to speed quickly
Someone has just joined your team and needs to get across what users have been saying about the area they'll be working on. Everything is in Userback, but pointing them to the inbox and saying "have a read" isn't particularly useful when there are hundreds of items. Ask your AI assistant to pull together a clear summary of the key themes, the most common issues, and anything still open. They'll be up to speed in minutes rather than spending a day reading through feedback on their own.
Try this prompt: Summarize all feedback in [Project] related to [feature or area]. What are the most common issues users have raised, what has been resolved, and what is still open? Write it as a briefing for someone new to the team.
Tips for Getting the Most Out of It
A few habits that make prompts work better:
Name the project. The more specific you are about which project, date range, or status you're interested in, the more focused and useful the results.
One task per prompt. Ask for a summary, or a triage, or a prioritization, not all three at once. You get better output and more control.
Review before applying changes. When MCP suggests updates to status, assignees, or comments, you approve each action. Nothing changes in Userback without your confirmation.
Iterate. Start broad ("what are the themes?"), then narrow in ("tell me more about the onboarding ones"). MCP holds context across the conversation.