Usero MCP, give your coding agent your user feedback

Hacker News

Usero is a feedback tool: widget, email, Slack and app reviews into one clustered inbox. I recently put an MCP server in front of it at https://usero.io/mcp , which has been really useful in my own workflow. To-date there are 36 tools (signup, clients, feedback, clusters, forms and theming, user tests, AI test runs). E.g. `list_clusters` returns clusters biggest first with up to three verbatim user quotes each. `request_ai_pr` has Usero write and open the pull request server-side on the connected repo. I used to hate MCP because of the bloat it gave your context, but since 2.1 Claude Code defers MCP tools, so the model sees tool names only until it loads one. Here's a full example transcript, with all tool calls: https://usero.io/blog/feedback-mcp-server#full-transcript Pricing: free tier is $0 with 5 AI PRs a month, Pro is $39 for 50, Team is $89 for 200. MCP is on every tier. Blog post: https://usero.io/blog/feedback-mcp-server . I'm building this because I think as building features gets easier, it becomes even more important for startups to listen to their users and focus on high-quality product decisions. My goal is making those product decisions as easy as possible, helping companies be UserObsessed. I'd love to hear your thoughts.

Visit product View on Hacker NewsSep 11, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, claude, model · Missing: mac, agents, macos

94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month, users · Missing: mobile apps, ios, personal

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

29%29% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: reviews, users, calls · Missing: plus, platform, intuitive

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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