"How to run a Python script in a sandboxed environment" matching MCP servers:

An AI recipe recommendation server based on the MCP protocol, providing functions such as recipe query, classification filtering, intelligent dietary planning, and daily menu recommendation.

Provides a personal hybrid running and strength training coach that integrates with Intervals.icu, offering 28-day plans, activity reconciliation, and delivery of scheduled workouts to calendars via MCP.

An MCP server that pulls your Strava data and gives you a run recommendation from Claude, rendered as a dashboard.

Enables AI assistants to access Oura Ring health data including sleep, readiness, activity, heart rate, and workouts through MCP tools with OAuth2 authentication.

Enables AI agents to rank US healthcare organizations for clinical trial site feasibility, predicting patient accrual for a Graves' disease protocol using claims and registry data.

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

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.

Gives any MCP-compatible AI assistant secure, read-only access to your personal Strava fitness data, including activities, segments, routes, gear, and more through natural language.

MyChart MCP Server

Enables AI assistants to interact with MyFitnessPal data including food diary, exercises, body measurements, nutrition goals, and water intake through natural language.

An MCP server that provides a USDA-accurate food database with deterministic macro math, enabling tools like list_foods, get_food, calculate_macros, filter_by_diet, build_meal, and list_available_tags. It allows Claude to look up nutrition facts and solve for exact macro targets offline, without AI or network.

MCP server for the Google Health API: heart rate, activity, sleep, SpO2, HRV, ECG and irregular-rhythm notifications, read into a local SQLite cache for fast offline queries and trend analysis. OAuth 2.0 with automatic token refresh, incremental sync, a cache-only offline mode, and a doctor command that diagnoses a setup without spending quota.

Enables users to describe workouts in plain English and have them created as structured Garmin Connect workouts with pace, HR, power, cadence targets, and repeat blocks. Also provides full workout lifecycle management including listing, updating, deleting, and scheduling to the calendar.

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.

MCP server that reads glucose data from a FreeStyle sensor via the LibreLinkUp API, providing current glucose and history with explicit coverage and gaps.

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.

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 Claude Desktop to query and analyze diabetes data from Omnipod 5 and Glooko through natural language, providing clinical audit and triage insights such as time in range, trends, and bolus analysis.

Privacy-first MCP server for querying personal health and finance data with AI. Built with Python, FastMCP, and SQLite. Local-first and open source.

SuperGlookoQuery is a Claude Desktop extension (MCPB) that connects to Glooko diabetes device data and exposes it as clinical analysis tools (time in range, GMI, trends, enriched bolus/basal analysis, charting), for any pump/CGM combination Glooko supports rather than one fixed device. It's a fork of Richard Hall's podquery-mcp, reworked to discover each account's own data shape rather than assumi