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DimensionPredictive Maintenance MCP ServerGive any AI assistant the ability to analyze vibration data, detect machinery faults, and generate professional diagnostic reports — through natural conversation.Hevy MCPManage your Hevy workout data from AI assistants via MCP.
FMRS34 / 100 · D77 / 100 · B
Reliability7 / 2012 / 20
Security and permissions6 / 2016 / 20
Maintenance8 / 2016 / 20
Documentation7 / 2018 / 20
Setup experience6 / 2015 / 20
Best for
  • Reliability & maintenance engineers wanting fast vibration diagnostics in plain language — no coding required.
  • Developers & industrial-AI practitioners who want to expose predictive-maintenance workflows as MCP tools.
  • Researchers & students working on bearing fault diagnosis, condition monitoring, or MCP/agent tooling.
  • Hevy PRO users who want AI assistants to directly access their workout data
  • People who prefer using MCP clients like Claude, Cursor, Codex for fitness tracking
  • Anyone needing summaries and insights from their training data
Not for
  • Domains outside vibration analysis and machinery health monitoring.
  • Fully autonomous decision-making without expert oversight (it supports, not replaces, experts).
  • Real-time streaming or multi-asset fleet monitoring (not yet implemented).
  • Users without a Hevy PRO subscription (API key required)
  • Users who want to use the server without an API key
  • Those needing delete workflows (Hevy API does not expose delete endpoints)
Required permissions
  • Local file system access: read signal files, manual PDFs, write reports.
  • Network access may be required for initial setup (e.g., pip install), but the tool operates locally.
  • Requires Python 3.11+ and `uvx` (uv) for package running.
  • Requires HEVY_API_KEY environment variable for Hevy API authentication
  • Can create, update, and replace workouts, routines, folders, templates, and body measurements via tools
  • Read operations can fetch workouts, routines, folders, templates, history, and user info
Risks and side effects
  • Diagnostic accuracy depends on signal quality and declared sampling rate/unit; incorrect inputs can yield misleading results.
  • Bearing catalog data must match actual bearings to compute fault frequencies correctly.
  • Computed results sent to the LLM could potentially expose sensitive machine data, though raw signals stay local.
  • Anomaly detection models may overfit to training baselines and misclassify novel fault patterns.
  • API key can be misused if leaked; do not expose in URLs, logs, or screenshots
  • Create operations may produce duplicates on retry; update operations replace existing records
  • The server sends data to the Hevy API and may send telemetry to external services unless disabled
Supported clientsClaude Desktop, Claude Code, VS CodeClaude Desktop, Cursor, Codex, Antigravity
Tools3326