super-hevy-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@super-hevy-mcpshow my latest workout with details"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
super-hevy-mcp
MCP server for the Hevy workout tracker — full API coverage, built for serious lifters. Ask Claude about your training history, log workouts, and manage routines straight from chat.
Requirements
Node.js 20+
A Hevy Pro subscription (the Hevy API requires Pro)
A Hevy API key: https://hevy.com/settings?developer
Setup
Claude Code
claude mcp add super-hevy -e HEVY_API_KEY=<your-key> -- npx -y super-hevy-mcpClaude Desktop
Add to claude_desktop_config.json:
{
"mcpServers": {
"super-hevy": {
"command": "npx",
"args": ["-y", "super-hevy-mcp"],
"env": { "HEVY_API_KEY": "<your-key>" }
}
}
}Tools (v0.3)
Tool | Description |
| Your Hevy profile |
| Logged workouts, most recent first ( |
| One workout with all exercises and sets |
| Total workouts on the account |
| Workout updates/deletes since a date (for syncing) |
| Log a completed workout |
| Replace a workout's contents |
| Saved routines ( |
| One routine with prescribed sets |
| Create a routine |
| Replace a routine's contents |
| Exercise library (built-in + custom), |
| One exercise template by ID |
| Create a custom exercise template |
| Folders used to organize routines ( |
| One routine folder by ID |
| Create a routine folder |
| Every logged set for one exercise, across workouts (progression analysis) |
| Logged body measurements ( |
| One measurement entry by date |
| Log a new measurement entry for a date |
| Replace a measurement entry's contents |
| Top-set/e1RM trend for one exercise, by session |
| Working sets/tonnage per muscle group per week |
| Sessions per week and days/week trained per muscle group |
Weights are kilograms. Workout sets accept RPE (6-10); routine sets accept
rep_range targets. Responses are trimmed (no null noise) to keep context lean.
Analysis (v0.3)
Server-side aggregation tools that pre-crunch training metrics instead of making the LLM re-derive them from raw workout history.
get-progressive-overload-report— "Is exercise X progressing?" FetchesGET /v1/exercise_history/{exerciseTemplateId}over aweeks-long window (default 12, 1-52), groups the logged sets by session (workout), and reports per session: top working-set weight, best estimated 1-rep max (e1RM), working-set count, and tonnage. Also returns an overalltrend(first vs. last session e1RM, delta, % change, and the window's peak e1RM). Warmup sets are excluded from every metric — only working sets (normal/failure/dropset) count. e1RM uses the Epley formula:weight_kg * (1 + reps / 30).get-volume-by-muscle-group— "How many working sets / how much tonnage per muscle group per week?" (a volume-landmark check). PagesGET /v1/workoutsover aweeks-long window (default 8, 1-52), attributes each working set to its exercise's primary muscle group (viaGET /v1/exercise_templates/{id}, one fetch per distinct exercise used), and buckets sets/tonnage by ISO-8601 week. Only working sets (normal/failure/dropset) count — warmups are excluded. Tonnage is externalweight_kg × repsonly (bodyweight lifts still count as sets, at 0 tonnage). Exercises with no known muscle-group mapping are bucketed as"unknown"rather than dropped. Reports each muscle group'sweekly_avg_sets/weekly_avg_tonnage_kg(averaged over the full requested window, so they're comparable to published volume landmarks) plus a per-week breakdown, sorted most-trained first.coverage.truncated: truemeans the workout fetch hit its page cap before covering the full window.get-training-frequency— "How many sessions per week, and how often do I train each muscle group?" PagesGET /v1/workoutsover aweeks-long window (default 8, 1-52).sessions/weekly_sessions_avgcount distinct workouts per ISO-8601 week. For each muscle group,avg_days_per_weekcounts distinct calendar days with at least one working set targeting that muscle's primary group (warmups excluded; training a muscle twice in one day still counts as one day). Averages are totals divided by the full requestedweekswindow.muscle_group_frequencyis sorted most-trained first.
v0.3 completes the bodybuilding analysis layer (progressive overload, volume, and frequency). Roadmap: MCP prompts for guided workflows, and body-composition analytics.
Development
npm install
npm test # unit + integration tests (mocked HTTP)
npm run smoke # end-to-end against the real API (needs HEVY_API_KEY)
npm run buildLicense
MIT
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Maintenance
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