Official MCP server for Arvo - AI workout coach. Access your training data through Claude Desktop, Cursor, and other MCP clients.
Official MCP server for Arvo - AI workout coach. Access your training data through Claude Desktop, Cursor, and other MCP clients. That is what the arvo mcp mcp server brings to an AI assistant: the same capability, reachable through the Model Context Protocol rather than a separate app or dashboard.
Perfect for fitness enthusiasts who want to: - 🏋️ Ask "What's my workout today?" and get your personalized training plan - 📊 Track gym progress with AI-powered insights - 💪 Monitor PRs, volume, and muscle group distribution - 📈 Analyze body composition trends over time
MCP is an open protocol that enables AI assistants like Claude to securely access external data sources. arvo-mcp lets Claude access your workout data, training history, and fitness metrics through natural conversation.
The server publishes 14 tools. What each one is for:
get_user_profile — Get your fitness profile, experience level, and preferencesget_active_split — Get your current training split and scheduleget_recent_workouts — View your most recent completed workoutsget_workout_for_day — Get the workout for any cycle dayget_workout_stats — Get aggregated training statisticsget_active_insights — View AI-generated training insightsget_personal_records — See your PRs for each exerciseget_exercise_progress — Track progression for specific exercisesget_exercise_video — Get demonstration videos for exercisesget_volume_by_muscle — View volume distribution by muscle groupget_coach_info — Get your coach's informationget_coach_notes — View notes from your coachConfiguration is passed through the environment: ARVO_API_KEY, ARVO_BASE_URL. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
The server ships on npm as arvo-mcp, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.
Among the developer tooling options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Arvo MCP's toolset — get_user_profile, get_active_split, get_recent_workouts and 11 more — is a fair guide to whether it matches your workflow. It is maintained by khaoss85; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Arvo MCP's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| get_user_profile | Get your fitness profile, experience level, and preferences |
| get_active_split | Get your current training split and schedule |
| get_recent_workouts | View your most recent completed workouts |
| get_workout_for_day | Get the workout for any cycle day |
| get_workout_stats | Get aggregated training statistics |
| get_active_insights | View AI-generated training insights |
| get_personal_records | See your PRs for each exercise |
| get_exercise_progress | Track progression for specific exercises |
| get_exercise_video | Get demonstration videos for exercises |
| get_volume_by_muscle | View volume distribution by muscle group |
| get_coach_info | Get your coach's information |
| get_coach_notes | View notes from your coach |
| get_approach_details | Learn about your training methodology |
| get_body_progress | Track body composition changes |
{
"mcpServers": {
"arvo": {
"command": "npx",
"args": ["-y", "arvo-mcp"],
"env": {
"ARVO_API_KEY": "arvo_your_api_key_here"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| ARVO_API_KEY | Credential the server authenticates with. | Yes |
| ARVO_BASE_URL | Endpoint or connection string the server talks to. | Yes |
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