Freddy MCP Server: Give Your OpenClaw Agent Read Access to Personal Health Data
Freddy is a personal health data server that exposes your health and performance data as an MCP server. It just went live as an iOS and Android app, meaning your OpenClaw agents can now pull real sleep, recovery, and training load data instead of relying on generic advice.
What It Connects
- Apple Health or Health Connect (from phone)
- Oura, Polar, WHOOP, Garmin, Withings, Suunto
- Intervals.icu, Hevy
You connect your sources once, and the agent gets access to all of it inside whatever workflow you're running.
Auth for Headless Agents
The recommended auth flow for headless agents is the device flow. This gives the agent its own scoped access without you having to paste a URL around.
Weekend Project Idea
Build a morning agent that looks at last night's sleep and recent training load, then writes you a two-line note on whether to push or back off today. It's a small task but reads real numbers instead of guessing.
You can find Freddy at https://freddy.coach/.
Privacy
Data is encrypted, not sold, and the developer is not looking to profit off your stats. If you don't trust third-party solutions, just don't use it.
📖 Read the full source: r/openclaw
👀 See Also
Multi-Agent Loop Failures Are Org-Design Failures, Not Prompt Failures
Agent loops bouncing between peers aren't prompt bugs—they're org-chart problems. Treat agent networks as hierarchies with clear stop authority.
Claude Code's Plan-Skeptic Sub Agent Identifies Security Gaps in Generated Plans
A developer discovered Claude Code's plan-skeptic sub agent, which identifies gaps and issues in AI-generated development plans, particularly catching security concerns that weren't initially obvious. The agent works alongside the previously known security-sheriff sub agent to improve plan quality.
AI Functions: Runtime Code Generation with Automated Verification
AI Functions is a Python library that lets you define functions with natural language specifications instead of implementation code, executes LLM-generated code at runtime, and validates outputs with post-conditions that trigger automatic retries on failure.
Fine-tuned Qwen3.5-2B with RAG-Engram architecture improves grounded answer accuracy from 50% to 93% at 8K context
A developer fine-tuned Qwen3.5-2B with a custom RAG-Engram architecture to address the 'lost in the middle' phenomenon, improving correct answers at 8K tokens from 50% to 93% on real-world queries. The system uses a two-level approach with static entity embeddings and dynamic chunk navigation.