Built a No-code platform to build and deploy MCP Servers

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Most of the LLMs behave like crap when vibecoding for MCPs. Either the libraries used are totally outdated or confuses MCP with "Microsoft Certified Professional". I built this during the MCP hackathon by YC. And got some good traction during the hackathon itself (100+ users tried, 27 of them 1+ hours). I have a separate deployment platform at leanmcp.com to deploy these MCPs. And a testing platform to test the MCPs out. Would love to get some feedback from you guys. I kind of see mostly developers using it. I am in a bit of crossroads. Should I just focus on hard-core cracked developers? Or may be focus on vibe coders as well. Any feedback is appreciated.

Visit product View on Hacker NewsMay 27, 2025

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mcp, user, using · Missing: mac, agents, macos

90%90% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

34%34% 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

31%31% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

18%18% 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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