"Notion - Productivity and Note-Taking App" matching MCP servers:

A multi-domain productivity tool that currently enables job search automation, application tracking, and pipeline analysis for Career management. Future phases plan to add health tracking, workout management, and family expense monitoring capabilities.

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.

An MCP server featuring an interactive React UI for Garmin Connect integration. It allows users to access and interact with Garmin fitness and health data through the Model Context Protocol.

MCP server that connects WHOOP health tracker data to ChatGPT, enabling read-only search, fetch, summary, and a React dashboard widget for recovery, sleep, strain, and workouts.

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

Enables logging workouts, sets, routines, food, water, and macros through natural-language chat in any MCP client, with OAuth-based secure access and timezone-aware daily tracking.

A read-only MCP server for the WHOOP API v2 that lets you query and analyze your own recovery, sleep, strain, cycles, and workout data. Note: currently a pre-alpha scaffold with stubbed internals.

Enables users to interact with the Griptonite indoor climbing app, allowing them to log climbs, browse routes and grades, and retrieve activity feeds and rankings.

MCP server for the Hevy fitness tracking app, enabling management of workouts, routines, exercise templates, folders, and webhooks through AI assistants.

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).

Apple HealthKit → PostgreSQL sync + MCP server for Claude Code, Cursor, and Ollama. Open-source iOS app with reliable background sync (HKObserverQuery). 11 tools covering health summary, sleep, HRV trends, workouts, daily snapshots, metric stats, period comparison, and coaching briefs. Supports Supabase, Neon, and self-hosted Postgres. Privacy-first: your data goes to your own database.

Enables querying health data from the Health Auto Export app on iPhone/iPad via MCP, allowing LLMs like Claude to access and analyze personal health metrics.

Enables Claude to access your MyFitnessPal diary—foods, portions, meal and daily nutrition totals, water, exercise, steps, and weight—through a custom connector on web, desktop, and mobile.

A Python-based Model Context Protocol server that provides AI assistants with access to Oura Ring health and fitness data across 15+ endpoints. It enables querying metrics like sleep, activity, readiness, and cardiovascular health through simple authentication and date-range filtering.

MCP server for read-only access to Xiaomi Mi Fitness shared family health data, enabling queries for family members, health summaries, and historical metrics via ChatGPT.

Enables LLMs and MCP clients to securely access your Garmin Connect health and running data, including daily wellness metrics, activity details, training status, and race predictions, for personalized coaching and analysis.

Connects to Garmin Connect and exposes your fitness and health data to OpenWebUI, Claude, or any MCP-compatible client via Streamable HTTP transport.

Enables querying Yazio food logs including meals, daily summaries, and nutrition totals through MCP tools.

MCP server for Hevy that fetches live workout data and computes analytics like e1RM, PRs, volume, and consistency, while enabling writes for routines, folders, and body measurements without modifying workout history.

Unofficial MCP server for Hevy that lets AI assistants design training programs, create routines directly in your Hevy account, and analyze logged workouts.