"Methods to Store and Learn Dynamic Context Memories" matching MCP servers:

Enables MCP clients to connect to a privacy-first, self-hostable workout planning and training log, allowing coaching agents to preview and apply program changes while accessing training data through OAuth-protected endpoints.

MCP server for tracking nutrition meals and workouts, integrating with claude.ai to manage food logs, macros, exercise catalogs, and generate daily/weekly summaries.

Provides Claude with real-time access to local health data including sleep, recovery, strain, and workouts from WHOOP and Apple Health, enabling informed context-aware interactions.

MCP server to read daily activity, sleep, heart rate, and body metrics from Google Health API, allowing AI assistants like Claude to access your health data. Optionally syncs health metrics to an Obsidian vault.

Enables read-only access to RENPHO Health smart scale measurements through MCP, allowing users to retrieve latest readings, body composition history, and weight trends via an MCP client.

Enables AI assistants to access and analyze Polar device health data, including exercises, heart rate, sleep, and recovery metrics, by preprocessing raw sensor data into compact summaries to avoid context window overload.

A Model Context Protocol server for TrainingPeaks with an analytics focus — enabling real-time querying of training data, performance trends, CTL/ATL/TSB analysis, and training load optimization through Claude Desktop.

Enables MCP clients to query AimHarder for authenticated account context, discover accessible gyms, and select a gym for queries.

Connects Claude to your Strava fitness data, enabling natural language queries about your training history such as activities, stats, and routes.

Exposes Strava training data to Claude for coaching, planning, and analysis via a read-only MCP server with OAuth and Docker support.

Connects AI agents to Iridium fitness data to query workout history, nutrition logs, and body measurements. It enables users to track exercise progress, training volume, and personalized trainer analysis through natural language.

A Model Context Protocol (MCP) server for Garmin Connect integration. Access your activities, health data, training metrics, and more through Claude and other LLMs.

Exposes real-time health data from the Ultrahuman Ring to AI agents via the Model Context Protocol, providing optimized summaries with trend analysis and actionable insights.

Connects Sensor Bio wearable data to AI assistants via the Model Context Protocol, enabling queries about sleep, heart rate, activity, and other biometrics.

Enables AI assistants to read and log MyFitnessPal nutrition data, including tracking calories, macros, searching foods, and adding meals through natural conversation.

A lightweight Garmin Model Context Protocol server with 12 curated endpoints for endurance athletes, enabling natural-language queries of activity, training, health, device, and goal data from Garmin Connect.

A Model Context Protocol server that connects Claude to Hevy workout logs, enabling workout reading, routine creation, and training analysis.

Enables AI assistants like Claude to interact with TrainingPeaks, allowing users to query workouts, build structured intervals, track fitness trends, and add comments through natural language, with automatic secure login.

A Model Context Protocol (MCP) server implementation that interfaces with the Hevy fitness tracking app and its API. This server enables AI assistants to access and manage workout data, routines, exercise templates, and more through the Hevy API (requires PRO subscription).

Enables agents to locally analyze Huawei Health export data, retrieve activity summaries, tracks, samples, and health context, and generate structured report evidence with optional environmental data.