Freddy MCP Server Connects Wearables to AI Agents with Headless Sign-In
Freddy is an MCP server that connects wearables and health platforms to any AI client supporting the MCP protocol. It now supports headless sign-in, letting autonomous agents (Claude Code, OpenClaw, Cowork, Cursor, custom agents) perform account actions on a schedule: connect a wearable, trigger a sync, read the audit log, manage subscription.
Supported Devices & Clients
- Wearables/Platforms: Polar, Oura, Withings, Suunto, Intervals.icu, Hevy (plus WHOOP, Strava, Dexcom in beta)
- AI Clients: Claude Desktop, Claude.ai, ChatGPT, Notion AI, Perplexity, Claude Code, OpenClaw, Cowork, Cursor — any client that speaks MCP
Headless Sign-In for Scheduled Workflows
Previously only interactive sign-in via OAuth was possible. Now agents can authenticate headlessly, enabling fully automated recurring tasks like:
- Morning briefing pushed to Telegram
- Daily summary written into Notion
- Monthly reports on training load, recovery, and sleep trends, sent wherever you read
The operator reports running these workflows with his personal agent, which already knows his baseline and goals and can act without starting from scratch each time.
Privacy & Data Handling
Health data is encrypted. The operator states he does not sell data and is not looking to profit from user stats. The server is run by the same person behind fitIQ, with years of handling sensitive health data.
Site: https://freddy.coach/
📖 Read the full source: r/ClaudeAI
👀 See Also
Building CLIs for AI Agents: Design Principles from Google's gws CLI
Google's gws CLI demonstrates how to design command-line interfaces specifically for AI agents, prioritizing raw JSON payloads over human-friendly flags and implementing safety rails against hallucinations.
Offload Routine Claude Code Work to Gemma via offload-mcp MCP Server
offload-mcp is a new MCP server that lets Claude offload routine tasks like commit messages and PR summaries to free model APIs (default Gemma via Google GenAI API), saving Claude tokens and running lightweight work without local hardware constraints.
Benchmarking Nemotron 3 Super 120B with 1M token context on M1 Ultra
A user tested Nemotron 3 Super 120B with a Q4_K_M quantized model using llama.cpp on an M1 Ultra, achieving a 1 million token context window that consumed approximately 90GB of VRAM. Performance benchmarks show token generation speeds ranging from 255 t/s at 512 prompt processing down to 22.37 t/s at 100,000 token context.
Claude-Code v2.1.111 adds Opus 4.7 xhigh effort, /ultrareview, and PowerShell tool
Claude-Code v2.1.111 introduces the Opus 4.7 xhigh effort level between high and max, adds the /ultrareview command for cloud-based multi-agent code reviews, and begins rolling out PowerShell tool support on Windows. The update also includes interactive /effort controls, auto theme matching, and numerous bug fixes.