"Files" matching MCP servers:

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.

MCP server that lets you ask questions about your Garmin training history in plain language. It ingests FIT files into DuckDB locally and exposes typed tools for analyzing activities, streams, and summaries.

Enables an LLM to query your personal health and finance data stored locally in SQLite files.

Local-first MCP server that reads Apple Health export files (export.xml/zip) and exposes activity, sleep, HRV, and workout data to AI agents, keeping all data on your machine.

Enables users to query Apple Health metrics, workouts, and trends from CSV files exported via the Health Auto Export app. It allows MCP clients to analyze health data such as heart rate, sleep stages, and activity levels directly from local iCloud Drive storage.

An MCP server for macOS that shares selected folders with NeuralNexus, enabling AI avatars to read local files and analyze Apple Health data via a secure outbound relay.

MCP server for workout-file conversion: convert, validate, and inspect FIT, TCX, ZWO, and Garmin Connect files, with round-trip-safe conversions through the KRD canonical JSON format. Runs locally over stdio via npx -y @kaiord/mcp. Part of the open-source, local-first Kaiord training platform.

Enables reading and analyzing Garmin Connect data—activities, health metrics, FIT files, and challenges—through natural language, for AI assistants like Claude and ChatGPT.

Enables AI assistants to retrieve and analyze Garmin Connect health data, including daily summaries, sleep, HRV, workouts, and raw FIT files, while also supporting SQL queries on the underlying database.

Provides read access to Garmin Connect health and training data (sleep, heart rate, stress, HRV, activities) and supports writes like creating workouts, editing activities, and exporting files.

Enables natural-language-driven human-centric lighting analysis, including CIE S 026 α-opic and circadian metrics, spectral sky reconstruction from EPW files, and Radiance daylight/electric/glare simulation.