Freddy CLI: Connect Health Data to AI Agents via MCP
Freddy Coach shipped a CLI tool that connects wearable health data to AI agents via the Model Context Protocol (MCP). It's open source (MIT) and handles the OAuth device flow, token refresh, and MCP plumbing so you don't have to roll your own.
Installation and Setup
npm i -g freddy-coach/cli
freddy login # device-flow sign-in, tokens in ~/.freddy (0600)
freddy connect oura # prints a URL to link a wearableKey Commands
freddy providers— list connectable wearablesfreddy metrics— what you can queryfreddy query --metrics hrv,sleep_duration --days 7— get datafreddy profile | freddy status— check connectionfreddy sync oura— force sync for a wearablefreddy disconnect oura— remove device
Every command accepts --json for agent-friendly parsing. Access tokens last an hour and refresh automatically for long-running agents.
Supported Data and Devices
The CLI reads sleep, HRV, resting HR, recovery, training load, workouts, body composition, and more. Currently available: Polar, Oura, Withings, Suunto, Intervals.icu, Concept2, Wahoo, Hevy. In beta: WHOOP, Garmin, Dexcom, Strava, Ultrahuman.
MCP Server Under the Hood
The CLI wraps the Freddy MCP server. The same data is accessible from any MCP client (Claude, ChatGPT, etc.) — the CLI is just for terminals and agents that prefer shell commands.
Find it at freddy.coach or npm as freddy-coach/cli.
📖 Read the full source: r/openclaw
👀 See Also
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Claude Code skill generates App Store screenshots using Gemini AI
A new Claude Code skill called /aso-cosmicmeta-ss creates App Store and Google Play screenshots through a 6-phase workflow that analyzes codebases and uses Gemini AI for enhancement. The skill includes an approval gate to catch layout issues before using API credits.