Oura MCP Server: Connecting AI Applications to Vital Health Data

Overview: What is Oura MCP Server?

The Oura MCP Server serves as an intermediary between AI applications and the Oura API, enabling seamless access to sleep, readiness, and resilience data through the Model Context Protocol (MCP). By leveraging our server, developers can integrate specific health data into a wide range of AI applications, enhancing their functionality and user experience. The Oura server implements MCP, ensuring compatibility with various MCP clients such as Claude Desktop, Continue, and Cursor.

🔧 Core Features & MCP Capabilities

The Oura MCP Server provides essential features tailored for AI integration. It supports date range queries to retrieve historical data across a specified period and today's data queries to get real-time insights. Each query function is meticulously designed to ensure accurate and consistent data retrieval. Our implementation details include:

Date Range Queries:
- get_sleep_data(start_date: str, end_date: str): Retrieves sleep data for the given date range.
```
def get_sleep_data(self, start_date: str, end_date: str):
    """Fetches sleep data between the specified dates."""
    # Implementation details
```
- get_readiness_data(start_date: str, end_date: str): Retrieves readiness data for the given date range.
- get_resilience_data(start_date: str, end_date: str): Retrieves resilience data for the given date range.
Today's Data Queries:
- get_today_sleep_data(): Retrieve sleep data for today.
```
def get_today_sleep_data(self):
    """Fetches sleep data for today."""
    # Implementation details
```
- get_today_readiness_data(): Retrieve readiness data for today.
- get_today_resilience_data(): Retrieve resilience data for today.

⚙️ MCP Architecture & Protocol Implementation

Oura MCP Server's Implementation of MCP Protocol

The Oura MCP Server is meticulously architected to adhere to the Model Context Protocol standards. The protocol flow ensures secure and efficient communication between AI applications, our server, and the Oura API. This architecture guarantees:

Data Security: HTTPS connections are established to protect sensitive data during transmission.
Scalability: Efficient caching mechanisms handle large volumes of queries without performance degradation.

Mermaid Diagram: MCP Protocol Flow

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Oura API]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

🚀 Getting Started with Installation

To install and set up the Oura MCP Server, follow these steps:

Obtain an API Token:
- Navigate to the Oura Developer Portal and create a Personal Access Token.

Install Dependencies:

# Using pip for Python package management
pip install oura-mcp-server

Configure MCP Client Integrations:

Update your claude_desktop_config.json (located in ~/Library/Application\ Support/Claude/claude_desktop_config.json on macOS and %APPDATA%/Claude/claude_desktop_config.json on Windows) to include:

{
    "mcpServers": {
        "oura": {
            "command": "uvx",
            "args": [
                "oura-mcp-server"
            ],
            "env": {
                "OURA_API_TOKEN": "YOUR_OURA_API_TOKEN"
            }
        }
    }
}

💡 Key Use Cases in AI Workflows

Real-Time Sleep Analysis

Developers can integrate Oura MCP Server to provide real-time sleep analysis within their applications. This allows users to monitor and understand their sleep patterns, leading to improved health outcomes.

# Example of getting today's sleep data
data = oura_mcp_client.get_today_sleep_data()
print(data)

Historical Data Analysis for Insights

Historical data analysis can reveal trends over time. For instance, a development team might analyze past sleeping patterns to identify irregularities and make informed adjustments.

# Example of getting historical sleep data
start_date = "2023-05-01"
end_date = "2023-05-07"
data = oura_mcp_client.get_sleep_data(start_date, end_date)
print(data)

🔌 Integration with MCP Clients

The Oura MCP Server is compatible with several AI applications and clients. Below is a compatibility matrix:

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

📊 Performance & Compatibility Matrix

The Oura MCP Server ensures robust performance and compatibility with diverse environmental configurations. It integrates seamlessly into various AI workflows, delivering consistent data retrieval across different platforms.

🛠️ Advanced Configuration & Security

Security Considerations

Environment Variables: Securely store API tokens in environment variables to prevent exposure.
Error Handling: Custom error messages enhance user experience by providing clear feedback during failures.

Mermaid Diagram: Data Architecture

graph TD
    A[Data Source] --> B[Caching Layer]
    B --> C[MCP Server]
    C --> D[Client Application]
    style A fill:#e8f5e8
    style D fill:#e1f5fe

❓ Frequently Asked Questions (FAQ)

Q: How do I integrate the Oura MCP Server with Claude Desktop?

A: To integrate the Oura MCP Server with Claude Desktop, update your claude_desktop_config.json file to include:

{
    "mcpServers": {
        "oura": {
            "command": "uvx",
            "args": [
                "oura-mcp-server"
            ],
            "env": {
                "OURA_API_TOKEN": "YOUR_OURA_API_TOKEN"
            }
        }
    }
}

Q: What are the API format restrictions for retrieving sleep data?

A: Sleep data requests should use ISO date formats (YYYY-MM-DD). Any deviation may result in invalid date errors.

Q: Can I customize error messages returned by the Oura MCP Server?

A: Yes, you can modify error handling logic to provide custom error messages based on specific needs and user feedback.

Q: Does the Oura MCP Server support multiple AI clients simultaneously?

A: Absolutely. Our architecture is designed to manage concurrent connections from various MCP clients, ensuring seamless operation across a wide range of applications.

👨‍💻 Development & Contribution Guidelines

Contributions are welcome! To get started:

Fork the Repository:
- Visit https://github.com/tomekkorbak/oura-mcp-server and fork it.

Clone Your Forked Repository:

git clone https://github.com/YOUR_USERNAME/oura-mcp-server.git
cd oura-mcp-server

Install Dependencies:
```
pip install -e .
```
Run Tests:
```
pytest
```
Make Changes & Commit:

🌐 MCP Ecosystem & Resources

Explore the broader MCP ecosystem and resources:

Official MCP Documentation: Access comprehensive guides at Model Context Protocol.
MCP Client Library: Utilize pre-built libraries available for different programming languages.
Community Forums: Engage with other developers in forums dedicated to MCP integration.

By leveraging the Oura MCP Server, AI application builders can harness valuable health data, enhancing their applications and empowering users to live healthier lives.

Oura MCP Server