Upload training plans defined in natural language into you Garmin watch

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I got tired of clicking through 47 menus just to create a simple interval workout in Garmin Connect. So I built garmin-workouts-mcp - a Python tool that turns Markdown files into Garmin workouts and syncs them automatically. As an amateur runner with decent fitness (VO2max 57), I decided to get serious about structured training this year. Problem: I started late, and Garmin's built-in plans are about as flexible as a brick. They wanted me doing base-building when I needed to be ramping up intensity. The web interface for creating custom workouts feels like it was designed by someone who hates both runners and UI design. The tool uses the MCP protocol to integrate with Claude code, so you can describe workouts in plain English. Write "5x1km at threshold with 2min rest" in a Markdown file, run the command, and it appears in Garmin Connect. I've included my own training_plan.md in the repo. Fair warning: it's cobbled together from various articles and optimistic assumptions about my abilities. Feel free to roast my questionable periodization choices and overly ambitious interval sessions.

Visit product View on Hacker NewsJul 22, 2025

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

82%82% 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

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

51%51% 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

46%46% 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

40%40% 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 · Strong signals: training · Missing: arr, mrr, revenue

24%24% 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.

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