delx-living-body

Meta-MCP that turns 15 wellness MCPs into one unified body data layer for AI agents.

Today, answering "should I train hard today?" forces an agent to orchestrate WHOOP recovery, Garmin Body Battery, Oura sleep, Nourish nutrition, and cycle phase across five separate MCP servers. That's brittle for users and confusing for the agent.

delx-living-body is one MCP server that:

1. Auto-detects which of 15 Delx Wellness connectors you already have installed locally — no manual config
2. Composes parallel calls to the right subset
3. Synthesizes a natural-language answer plus a structured reasoning trace and per-source confidence — using rule-based reasoning, no LLM calls

Install it once. Get a unified body data layer. Works with whatever wellness MCPs you already have.

If it helps your agent workflow, star the repo. Stars make the single-entry Delx Wellness path easier for other AI builders to find.

• Try it now, no accounts — npx -y delx-living-body demo ("Should I train hard today?")
• Run it in Claude · Cursor · ChatGPT · Hermes · OpenClaw — see agent setup examples
• Local-first — delx-living-body never reads your tokens; children read their own creds (privacy)
• Which connector should I use? — start at the Delx Wellness front door

The three flagship connectors this composes over: google-health-mcp (google-health-mcp-unofficial), garmin-mcp (garmin-mcp-unofficial), and wellness-nourish (wellness-nourish).

Install

npx -y delx-living-body

That's the whole install. No OAuth flow, no API keys — delx-living-body has no auth of its own. Each child connector handles its own credentials.

See the full agent demo → "Should I train hard today?" (no accounts needed)

npx -y delx-living-body demo

One command, no clone. The demo boots the real MCP server, fakes three installed connectors (WHOOP + Oura + Garmin, backed by a bundled stub child that carries synthetic body data), and drives it over stdio exactly the way an agent does. No real accounts, API keys, or network. Add --scenario=red to see the low-readiness ("back off today") path; demo --help lists options. Captured output lives at examples/demo-what-should-i-do-today.txt:

2) living_body_ask  question="What should I do today?"
────────────────────────────────────────────────────────────────
Recommendation:
   Today at a glance: recovery 74, sleep 83, body battery 68.

Confidence: high   Sources: whoop, oura, garmin

3) living_body_ask  question="Should I train hard today?"
────────────────────────────────────────────────────────────────
Recommendation:
   Green light for a hard session. Recovery and sleep both support high intensity.

Confidence: high   Sources: whoop, oura, garmin

Reasoning trace (rule-based, no LLM):
   Intent classified as: t

... [View full README on GitHub](https://github.com/davidmosiah/delx-living-body#readme)