Pierre
Provides AI-powered analysis of personal fitness data from multiple providers via an MCP server.
About
Pierre is a comprehensive Model Context Protocol (MCP) server designed for in-depth fitness data analysis. It offers secure access to personal fitness data from popular providers like Strava and Fitbit, enabling seamless integration with AI assistants such as Claude and GitHub Copilot. Users can leverage natural language prompts to gain nuanced insights into their activities, incorporating contextual factors like location, weather, and performance metrics for a truly intelligent understanding of their fitness journey.
Key Features
- Intelligent weather integration for real-time and historical environmental context
- 5 GitHub stars
- AI-powered activity intelligence for performance metrics, environmental context, and natural language summaries
- Advanced location intelligence with GPS-based detection, reverse geocoding, and trail identification
- Enhanced security with OAuth2 (PKCE), JWT authentication, and AES-256-GCM token encryption
- Multi-provider support for Strava and Fitbit data integration
Use Cases
- Compare cross-training activities, monitor heart rate zones, and track fitness improvement over time
- Gain comprehensive activity intelligence by correlating performance with weather, location, and personal records
- Analyze running pace trends, longest runs, and performance patterns with location context