Oura MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Oura as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Oura. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in Oura?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Oura MCP Server
Connect your Oura Ring to any AI agent and access your personal health data through natural conversation.
LlamaIndex agents combine Oura tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Sleep — Analyze sleep scores, stages (REM/light/deep/wake), efficiency, latency and timing
- Activity — Track daily steps, calories, MET minutes and activity goals
- Readiness — Monitor readiness scores, HRV, resting heart rate, body temperature and recovery
- Tags — Review your manual entries for mood, energy, behaviors and substances
- Workouts — Browse logged workouts with type, duration, calories and heart rate zones
- Heart Rate — Access detailed 5-minute heart rate samples and HRV data
The Oura MCP Server exposes 7 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Oura to LlamaIndex via MCP
Follow these steps to integrate the Oura MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 7 tools from Oura
Why Use LlamaIndex with the Oura MCP Server
LlamaIndex provides unique advantages when paired with Oura through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Oura tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Oura tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Oura, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Oura tools were called, what data was returned, and how it influenced the final answer
Oura + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Oura MCP Server delivers measurable value.
Hybrid search: combine Oura real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Oura to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Oura for fresh data
Analytical workflows: chain Oura queries with LlamaIndex's data connectors to build multi-source analytical reports
Oura MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect Oura to LlamaIndex via MCP:
get_activity
Supports date range filtering. Get your Oura activity data
get_heart_rate
Returns timestamp, heart rate value and HRV for each sample. Supports date range filtering. Note: This endpoint returns high-frequency data; use narrow date ranges. Get your Oura heart rate data
get_readiness
Supports date range filtering. Get your Oura readiness data
get_sessions
Includes overall scores, sleep metrics, activity data and readiness indicators. Supports date range filtering. Get your Oura session data
get_sleep
Supports date range filtering with start_date and end_date (YYYY-MM-DD). Pagination via nextToken. Get your Oura sleep data
get_tags
). Tags are user-entered data points that correlate with sleep and readiness scores. Supports date range filtering. Get your Oura tags
get_workouts
Workouts can be manually logged or auto-detected by the Oura Ring. Supports date range filtering. Get your Oura workout data
Example Prompts for Oura in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Oura immediately.
"How did I sleep last night?"
"What is my readiness score today?"
"Show me my activity from yesterday."
Troubleshooting Oura MCP Server with LlamaIndex
Common issues when connecting Oura to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpOura + LlamaIndex FAQ
Common questions about integrating Oura MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Oura with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Oura to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.