"How to search for people on LinkedIn by their name" matching MCP servers:

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 act as a personal health coach by connecting to Garmin wearable data and Notion workspace for automated calorie tracking, photo food logging, and coaching insights.

Connects WHOOP fitness data to Poke AI assistant, enabling natural language queries for recovery scores, sleep analysis, strain tracking, and healthspan metrics.

Exposes over 90 Garmin Connect tools for tracking activities, health metrics, and training data through the Model Context Protocol. It is optimized for Poke compatibility and supports deployment to Render via HTTP.

Enables AI agents like Claude Desktop, Cursor, and VS Code to connect directly to COLMI R02/R03 and compatible smart rings over local Bluetooth LE, exposing tools for ring scanning, battery and firmware status, activity and sleep/recovery insights, real-time heart rate and SpO2 vitals, 3-axis motion sampling, clock sync, and physical LED blinking. Runs fully locally with no vendor cloud, accounts, or subscriptions.

Connects AI clients to a personal Intervals.icu account to retrieve recent running activities as clean, normalized JSON. It supports read-only lookups filtered by number of runs and how many days back to search.

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.

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

MCP server for Cronometer nutrition tracking, enabling food diary management, nutrition data retrieval, food search, and biometric tracking via Cronometer's mobile REST API.

Enables AI agents to securely access Apple Health data (sleep, heart rate, menstrual cycle, etc.) via end-to-end encrypted local decryption from the Tether iOS app.

Query your Apple Health data including sleep, heart rate, steps, workouts, and more by ingesting an exported XML archive into a local SQLite database.

MCP server that enables AI assistants to access Whoop health data including recovery, sleep, workouts, and daily strain for personalized health recommendations.

This MCP server enables logging workouts in plain language through AI assistants and provides tracking of sessions, programs, records, estimated 1RM, and per-muscle load, with coaching informed by the user's logged data.

Enables an LLM to search a movement library, log single-movement and multi-movement workout results, and pull complete logged training history plus PR trends for Beyond the Whiteboard straight from a chat. All logged entries are posted privately, and session authentication can refresh automatically.

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.

Connects AI assistants to SmartGym on Mac, enabling users to review workouts, tweak routines, and build training programs in plain English, with changes syncing to iPhone and Apple Watch.

MCP server for Intervals.icu that enables AI assistants to manage athletic training data, including activities, calendar events, wellness metrics, and workout libraries.

MCP server that connects Claude or any MCP-capable LLM to your Garmin golf data, enabling natural-language queries on rounds, shots, strokes gained, club distances, course intel, and health-performance correlations from a private local DuckDB cache.

Minimalist Strava integration for Claude with 7 focused tools for querying athlete stats, activities, zones, and segment analysis.

An MCP server that provides access to detailed exercise-to-muscle mapping data for over 320 exercises and 65 muscles. It enables users to analyze workout coverage, find exercise alternatives based on muscle overlap, and search for specific exercise activation data.