OU
Data Analytics

Oura Ring MCP Server for Health Metrics

Access your Oura Ring data with an MCP server to fetch, analyze, and visualize health metrics for clearer insights and better wellness decisions.

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Quick Install
npx -y @rajvirtual/oura-mcp-server

Overview

This MCP (Model Context Protocol) server provides a developer-friendly bridge between Oura Ring health data and applications that need contextualized, queryable metrics. It pulls data from your Oura account and exposes it through a lightweight HTTP service that is optimized for use with LLMs, analytics pipelines, dashboards, and custom tooling. By centralizing access, the server simplifies authentication, rate-limiting, caching, and data transformation so downstream clients can focus on analysis and user experience.

The server is useful when you want to aggregate sleep, activity, readiness, and biometric traces into contextual payloads for models or analytical workflows. Typical scenarios include feeding recent sleep history into a recommender, producing weekly trend summaries for visualization, or powering conversational assistants that answer health-related questions using the user’s actual Oura metrics.

Features

  • Fetch Oura Ring health metrics (sleep, activity, readiness, biometrics) via the Oura API
  • Present contextualized metric payloads compatible with Model Context Protocol workflows
  • Simple token-based configuration and secure local deployment
  • Cache and normalize Oura responses for consistent downstream consumption
  • Docker-friendly for reproducible deployments and development
  • Lightweight HTTP endpoints and health checks for integration testing
  • Example client scripts and usage patterns to get started quickly

Installation / Configuration

Prerequisites: Node.js (14+ recommended), npm or Docker, an Oura personal access token or OAuth credentials.

Clone and install locally:

git clone https://github.com/rajvirtual/oura-mcp-server.git
cd oura-mcp-server
npm install

Create a .env file in the project root with your configuration:

# .env
OURA_ACCESS_TOKEN=your_oura_personal_access_token_here
PORT=3000
CACHE_TTL_SECONDS=300
LOG_LEVEL=info

Start the server locally:

npm run start
# or for development with autoreload
npm run dev

Docker usage:

# build the image
docker build -t oura-mcp-server .

# run with environment variables
docker run -d \
  -p 3000:3000 \
  -e OURA_ACCESS_TOKEN=your_oura_token \
  -e PORT=3000 \
  --name oura-mcp oura-mcp-server

Configuration notes:

  • OURA_ACCESS_TOKEN: personal access token or OAuth access token with appropriate scopes for reading user metrics.
  • PORT: HTTP port for the MCP server.
  • CACHE_TTL_SECONDS: how long to cache Oura responses to reduce API calls.

Available Resources

  • GitHub repository (source & examples): https://github.com/rajvirtual/oura-mcp-server
  • Oura API documentation: https://cloud.ouraring.com/docs/
  • Model Context Protocol (MCP) — general spec and integration patterns: refer to the MCP docs used by your LLM provider
  • Example client scripts: see the repo’s examples/ directory for sample fetch and prompt flows

If you need to extend the server, the repository includes starter modules for:

  • Authentication adapter (swap between personal token or OAuth refresh)
  • Data normalizers (convert Oura payloads into simplified metric objects)
  • Integrations hooks (webhooks, alerting, or direct ingestion into analytics stores)

Use Cases

  • Generate weekly sleep summaries for a personal health assistant

    • Example: Pull the last 7 days of sleep scores and sleep stage durations, aggregate averages, and feed the summary to an LLM to produce personalized sleep hygiene advice.

  • Trend detection and visualization

    • Example: Periodically fetch resting heart rate and HRV, cache values, and expose a JSON endpoint that a dashboard can graph to show long-term trends and anomalies.

  • Contextual prompts for conversational agents

    • Example: When a user asks “Why do I feel tired today?”, fetch the last 3 nights of sleep and recent activity, supply that context to the model, and let it generate targeted recommendations.

  • Health monitoring and alerts

    • Example: Compare today’s readiness score to a baseline; if the drop exceeds a threshold, trigger a webhook or notification for coach review.

Quick example: Fetching a metric

A simple curl call (replace HOST and token values as configured):

curl -H "Authorization: Bearer $OURA_ACCESS_TOKEN" \
  "http://localhost:3000/metrics?start=2026-03-01&end=2026-03-07"

The server will return normalized JSON with the requested time range, suitable for ingestion into analytics or model prompt-building.

Next steps

  • Review the repo examples to see common prompt and visualization patterns.
  • Configure a small dashboard (Grafana/Metabase) or notebook (Jupyter) to visualize exported endpoints.
  • If you plan to run multi-user deployments, integrate OAuth and a secure datastore for per-user tokens and consent management.
Tags:data-analytics