MCP Jetpack – The easiest way to get started with MCP in Cursor

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My friends and I built MCP Jetpack which makes it really easy to start using MCP. It’s an MCP server that automatically finds and executes the right tools needed to accomplish your task without having to manage MCP servers for each service (GitHub, Linear, Atlassian, Notion etc.). Once you add it to Cursor (or any other AI app with MCP support), Cursor instantly gets access to a growing library of remote MCP servers without any extra setup. For services that require authentication, you will be asked to login the first time you ask your AI to interact with that service. Two problems we are trying to solve: Friction - Normally if you want to give Cursor access to GitHub, you have to install the right MCP server and login before you can use GitHub with Cursor’s chat. With MCP Jetpack, you can ask Cursor to list your GitHub issues, and it will automatically execute the right tool behind the scenes to accomplish your task. For services that require authentication, you will be asked to login the first time you interact with the service. However, it all happens within the Cursor chat so you never have to context switch and fiddle with Cursor’s settings. Tool Limits - Cursor warns you if you have more than 50 MCP tools installed as it says having more will degrade performance. However, just installing the GitHub MCP server itself adds 74 MCP tools. With MCP Jetpack, you get access to GitHub, Atlassian and 15 other services with just two tools: “FindTool” and “ExecTool”. Here are the 17 services we support today: GitHub, Atlassian, Canva, Linear, Notion, Intercom, Monday.com, Neon, PayPal, Hugging Face, Sentry, Square, Webflow, Wix, Cloudflare Docs, Cloudflare AI Gateway, Cloudflare Workers Bindings. We’ll continue to add more services as companies launch remote MCP servers. If yours isn’t listed and you’d like it to be added, please email us at team@mcpjetpack.com. MCP Jetpack is in alpha so please let us know if you run into any problems or have any feedback - thanks!

Visit product View on Hacker NewsJul 21, 2025

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

93%93% 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

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

55%55% 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

48%48% 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 · Missing: plus, platform, intuitive

37%37% 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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

Incorrect prediction on native model

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