"How to create draft emails using Gmail's support agent class" 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 assistants to convert raw and cooked weights for meat and poultry by cut and cooking method from USDA data, calculate how much raw meat to buy for cooked servings, and look up cooking yields and sample ranges.

Enables AI assistants to plan and track McDonald's visits by locating stores, estimating commute calories, recommending nutrition-targeted menu combinations, monitoring orders, running spaced-repetition vocabulary practice during waits, and recording progress in a local dashboard.

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.

Family meal planning run by your own AI agent: it proposes ten dinners each week, the household votes and leaves feedback from personal links, the top picks win, and a grocery list is built minus what's already in the pantry. Remote MCP server at https://agentchef.net/mcp; connect with an API key and say "Run Agent Chef."

A zero-dependency MCP suite exposing computational tools across metabolic, cardio-renal, chronobiology, neuro-recovery, and biomechanics domains. It enables clients to derive longevity and physiology metrics such as autophagy index, glucose-ketone ratio, glycemic variability, peripheral resistance, nocturnal BP dipping, DLMO clock time, melanopic lux, caffeine clearance, EEG fatigue entropy, and grip vitality age from user-supplied measurements.

Exposes wellness data such as sleep, activity, heart rate, and stress as MCP tools so AI agents can retrieve current vitals, pull historical records, and analyze trends. Supports simulated physiology along with Fitbit and planned Apple Health providers.

A local-first MCP server for querying multi-omic personal health data (genome, labs, wearables) with an honesty contract and progressive disclosure skills.

A universal fitness intelligence layer for AI assistants like Claude, ChatGPT, and Copilot, enabling user profiles, workout/diet plans, calendar scheduling, and gamification via MCP and REST APIs.

Enables turning any personal-assistant REST backend into Claude-ready tools via a single MCP server, providing 38 tools for communications, finance, health, and more.

Enables circadian-aware scheduling of cognitive work, ambient flow-state detection, and utility-gated interruption decisions so proactive personal-agent interventions respect focus and urgency.

Your AI running coach that actually remembers. An MCP server with persistent memory that connects your Strava data to Claude — tracking your zones, pace trends, injuries, and goals across sessions so coaching advice stays current.

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.

MCP server for headless care coordination, providing tools for case-aware provider search powered by ai-carematch.

Exposes MCP tools that compute longevity and metabolic metrics (autophagy index, GKI, MAGE), cardio-renal biomarkers (BUN/creatinine ratio, cardiac output and TPR, nocturnal BP dipping), chronobiology estimates (DLMO, melanopic lux, caffeine clearance), plus neuro-recovery and biomechanics scores. Runs as a zero-dependency Python JSON-RPC 2.0 stdio server so any MCP-compatible client can query these physiological domain calculators.

Enables estimating sweat rate and sodium loss, modeling subclinical dehydration cognitive deficits, and generating personalized hourly fluid and electrolyte replenishment schedules.

Enables computing composite glycemic load for multi-ingredient meals, modeling protein and fat attenuation of glucose spikes, and forecasting post-prandial fatigue and stable focus hours to guide meal and task scheduling.

Enables users to model Lally habit automaticity curves, estimate behavioral friction price elasticity, and derive minimum viable micro-habits that protect streaks on low-energy days.

Enables users to aggregate meal glycemic index/load, model protein and fat buffering, forecast post-prandial cognitive crash risk and focus hours, and receive macronutrient stabilization recommendations.

Enables calculation of binaural beat frequencies for target cognitive states and scheduling of phased focus and recovery entrainment protocols using stereo carrier pairings.

Enables modeling supplement pharmacokinetics, tracking elimination half-life and washout, evaluating competitive absorption conflicts, and generating optimized ingestion schedules.

Enables users to model cumulative sleep deficits, estimate cognitive impairment using Van Dongen PVT kinetics, audit social jetlag, and generate circadian-safe gradual sleep repayment schedules.

Enables users to model two-compartment caffeine elimination, estimate adenosine A1/A2A receptor occupancy and slow-wave sleep suppression, and schedule intake windows and cutoffs from wake and bedtime.

Enables users to model indoor CO2 accumulation using mass-balance differential equations, estimate cognitive performance impairment from CO2 levels, and schedule timed fresh-air purges to restore healthy ventilation.

Enables modeling of ocular light exposure, forecasting melatonin suppression delays from digital screens, and generating timed digital-sunset schedules for sleep readiness.

This MCP server enables users to model ocular light exposure and circadian melatonin onset, forecast screen-induced DLMO delays, and generate timed digital sunset schedules for light hygiene and sleep readiness.

Enables users to estimate VO2max from heart-rate ratios or Cooper 12-minute run distances, benchmark results against ACSM age and sex medians, and derive cardiorespiratory fitness biological age and all-cause mortality hazard ratios.

Enables users to forecast when habits become automatic, quantify how friction changes completion rates, and adapt routines into minimum viable micro-habits during low-energy days.

Enables organizing personal contacts into concentric social layers, estimating warmth decay via an exponential half-life model, and scheduling mindful reconnection touchpoints within a weekly cognitive bandwidth.

Enables healthcare benefits management by providing member eligibility verification, coverage details for medical/dental/vision/pharmacy services, and service coverage verification through natural language queries.