predictive-maintenance-mcp: An open-source MCP server that turns LLMs into condition monitoring assistants for reliability engineers

by LGDiMaggio · MCP Server · ★ 106

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install predictive-maintenance-mcp

pip install predictive-maintenance-mcp
git clone https://github.com/LGDiMaggio/predictive-maintenance-mcp.git

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 66/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

ai-agentanomaly-detectionbearing-fault-diagnosisclaude-agentsclaude-aicondition-monitoringenvelope-analysisfastmcpfault-diagnosisiiot

Quick Facts

Stars106
Forks33
LanguagePython
CategoryMCP Server
Quality Score66/100
Open Issues13
Last Updated2026-10-23
Created2025-11-11
Platformsclaude-code, mcp, python
Est. Tokens~17k

About predictive-maintenance-mcp

Predictive Maintenance MCP Server Give any AI assistant the ability to analyze vibration data, detect machinery faults, and generate professional diagnostic reports — through natural conversation. An open-source MCP server and predictive maintenance AI agent that turns LLMs into condition monitoring assistants.

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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