Industrial predictive maintenance: vibration analysis, bearing faults, ISO 20816-3
Industrial predictive maintenance: vibration analysis, bearing faults, ISO 20816-3. Exposed over MCP by the predictive maintenance mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
An open-source MCP server and predictive maintenance AI agent that turns LLMs into condition monitoring assistants. Engineers describe what they need in plain language; the AI calls the right analysis tools and delivers results — bearing fault detection, risk assessment, anomaly detection, and remaining useful life estimation. Also available as a Claude Code plugin with 8 diagnostic skills. It's designed to support and accelerate expert decision-making.
predictive-maintenance-mcp on PyPI is all you need. Most clients run it directly, so configuration is a few lines and a restart.
You will need one environment variable: UV_LINK_MODE. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
Plenty of developer tooling servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. It is maintained by LGDiMaggio; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
{
"mcpServers": {
"predictive-maintenance": {
"command": "uvx",
"args": ["predictive-maintenance-mcp"],
"env": {
"UV_LINK_MODE": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| UV_LINK_MODE | Configuration value read at startup. | Optional |
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