"How to break tasks into smaller steps" 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.

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.

MCP server that searches French recipes from Marmiton, reads ingredients and steps, and rescales quantities to any number of servings without requiring an API key.

MyChart MCP Server

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 AI assistants to answer food safety and shelf-life questions using USDA-grade data, with tools for shelf life, safety checks, storage tips, and waste cost calculations.

Provides SQL access to the ANSES Ciqual French food composition database with nutritional data for over 3,000 foods. Supports full-text search and bilingual queries for comprehensive nutrition analysis.

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.

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.

Real-time human state awareness for AI agents. Fuses cardiovascular, vocal, visual, and textual signals into a unified stress score (0-100). Streamable HTTP transport with 4 tools: ingest_signal, get_human_state, get_history, delete_subject.

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

Solar vitamin D: whether the sun where you are can make vitamin D right now, how many minutes your Fitzpatrick skin type needs for a target dose, which months of the year synthesis is possible at your latitude, and how much a sun session produced. Computed from solar geometry, a clear-sky UV model with ozone and altitude, and live Open-Meteo UV data.

An MCP server that transforms Garmin data into compact, verdict-first coaching reports for LLMs, computing metrics like CTL/ATL/TSB and ACWR to answer training readiness questions.

Runs entirely locally to provide calm, animated breathing sessions that open in a browser or as an artifact, with tools for matched or custom sessions, pattern listings, and a breathing-break prompt, while collecting no data.

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.

Brings Garmin training data into Claude via Intervals.icu, enabling querying of activities, wellness, and planned workouts, as well as creating structured workouts that sync back to the Garmin watch.

Aggregates and analyzes fitness data from multiple sources like Whoop and Strava through a modular adapter architecture. It enables users to monitor health metrics, track activities, and gain insights into sleep, recovery, and training performance.

Enables AI agents to sync WHOOP recovery, sleep, workouts, cycles, and body-measurement data into a local Postgres database and query it through fast, offline-capable MCP tools without repeatedly hitting WHOOP API rate limits.

Provides local, read-only MCP access to Google Health API v4, enabling users to query recorded workouts, heart-rate samples, and related health data through natural language in an MCP client.