"Coding tools to understand and manage a large codebase like MCP, CLINE, or ROOCODE" 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.

Enables MCP clients and AI agents to invoke deterministic fitness calculators for BMR/TDEE, body fat, one-rep max, macros, activity multipliers, powerlifting attempts, and natural muscular potential. Each tool reports consensus across published formulas, supporting consistent fitness and training analysis.

Enables a personal agent to read selected live Garmin Connect activity, readiness and device fields and, only after explicit approval of an exact preview, to create, schedule and request device synchronization of one fixed 20-minute running connectivity test over stdio. Login credentials never enter the model-facing tool surface, and no other workouts can be created, edited or deleted.

Enables AI agents to access and manage fitness programs, exercise catalog, workout resources, activity metrics, journaled plan edits, and change tracking through authenticated tools.

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

Exposes Garmin Connect health and activity data (steps, sleep, heart rate, etc.) via MCP tools, with built-in login and MFA support.

Enables ChatGPT and compatible AI clients to query read-only Garmin Connect data—including sleep, HRV, heart rate, Body Battery, wellness metrics, and activities—through a hosted OAuth-backed MCP connection using a personal API key.

Enables users to search and retrieve fitness exercise information from the Api Ninjas database. It supports filtering by exercise name, target muscle, type, and difficulty level.

Enables Claude to access and analyze Whoop health data including recovery, sleep, strain, and workouts through a custom MCP connector.

Provides local MCP tools to query your own Xiaomi Mi Fitness health data (sleep, workouts, heart rate, etc.) stored in a local SQLite database, enabling AI assistants to access personal health records without exposing credentials.

An MCP server that converts structured cycling workout specs into MyWhoosh .zwo and Garmin Connect workout files, with tools for validation, description, and rendering. It also includes skills for uploading workouts to both platforms.

Ask your Oura Ring about sleep, readiness, activity, stress and heart rate in ChatGPT or Claude, in any language. Read-only, 10 task-oriented tools; runs in a sandbox demo mode with no credentials.

Enables retrieval of InBody body composition data including profile, scan history, and full metrics via MCP tools.

Fitness AI - MCP server providing AI-powered tools and automation by MEOK AI Labs

Enables AI assistants to read your Strava training data and answer plain-language questions about activities, splits, and totals across sports like running, skiing, and hiking.

Enables an MCP client to read and write a personal Garmin Connect account: activities with per-set strength detail, daily health metrics (sleep, HRV, stress, steps, readiness), planned and completed workouts, and weigh-ins with body composition. It also logs finished strength sessions and body-composition readings as FIT files so they appear in Garmin Connect like natively recorded ones.

Enables AI agents to read and analyze your Boardsesh climbing logbook across boards like Kilter, Tension, and MoonBoard, with read-only access and optional credentials for personalized recommendations.

Exposes the Oura API v2 as an MCP server with tools to query all 19 data collections (e.g., sleep, heartrate) with automatic pagination and explicit truncation warnings.

Connects WHOOP fitness tracker data to AI assistants like Claude and ChatGPT, enabling natural language queries about recovery, sleep, strain, and trends.

A stateless MCP server that normalizes strength training logs into a deterministic canonical schema, enabling consumers to build analytics downstream without storing data server-side.