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An automation tool built upon MCPs

Hacker News

An automation tool built upon MCPs

I was thinking about how Model Context Protocol (MCP) servers are a standard interface to third party tools, and how it must have been annoying to integrate before for companies like Zapier. I wondered if I could make a tool that does the same thing but for free and locally. After making a prototype it felt like something that could be built out and released so I'm now on that journey! It's still very early stages, I'm wanting to take it on a journey similar to Postman flows (which is awesome). It's my first desktop app using Tauri, a lighter-weight alternative to Electron. It's also my first time using Rust, so that's been really fun! The goal here is to provide a way to take the tool calling chains done by LLMs and solidify them into programmatic tasks - so you don't waste tokens every time getting an LLM to do it.

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Actual performance

1points
1comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, mcp, context · Missing: mac, agents, macos
75%75% 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
67%67% 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
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
48%48% predicted probability of success on AppSumo, 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
35%35% 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
13%13% 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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