CoolPlugz – A Claude MCP that turns Jira tickets into a merge-ready PR

Hacker News

Hi folks, I've been a fullstack dev and solopreneur for 9 years and the last 3 I have been using AI and MCP related tools in my dev environment. Most recently I had a lot of work for a blockchain client and started using claude code more than usual. I connected jira and GitHub mcps inside claude to fetch context, but I still had to repeat myself a lot. I guided it on how to go about branching, finding repos, testing against CI and open merge-ready PRs. I also had to create a source-of-truth file for coding principles as well. I actually tracked this and I spent on avg 75 mins a day, repeating, copy-pasting and arguing with it to deliver a task. So I built this mcp orchestrator - Coolplugz - to basically feed in all the instructions and coding principles in an agentic-native way so I don't have to be involved in the process of going from a Jira ticket to a merge-ready PR so much. And it is portable which comes quite handy. It has been working quite well so far, and saved me a few hours per week already. You can try it out for free and let me know what you think in the comments.

Visit product View on Hacker NewsAug 25, 2026

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

96%96% 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: started · Missing: supports, reddit linkedin, podcasting

61%61% predicted probability of success on Indie Hackers, 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

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

nativeThis product was originally launched on this platform.

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

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

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

32%32% 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

28%28% 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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