AgentVoice – Get feedback from AI to improve your MCP

Hacker News

I wanted to share a tool we built to solve a specific frustration: AI agents (like Claude Code or Cursor) are becoming the primary consumers of our APIs, but they are "silent" users. Unlike human developers who file GitHub issues or post on Discord when a schema is confusing or an error message is cryptic, agents just fail, retry, or give up. We ran a trace on our own MCP server and found 9 distinct friction points in a single 10-minute session. Things like undocumented enum constraints and case-sensitivity issues that never showed up as "errors" in our standard logs. AgentVoice closes this loop by triangulating three sources: it runs an LLM observer over session transcripts to diagnose intent, pulls production telemetry to prove the scale of the failure, and adds a submit_feedback tool so agents can narrate their own friction in real-time. I’d love to get feedback on the approach, especially from anyone currently shipping MCP servers or agent-facing APIs.

Visit product View on Hacker NewsFeb 25, 2026

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

98%98% 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

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

30%30% 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: users · Missing: plus, platform, intuitive

26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

20%20% 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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