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predictive-maintenance-mcp
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predictive-maintenance-mcp

Active·★ 52·NOASSERTION·Updated 2026-07-13
★ Trending★ Workflow Automation★ Observability

This open-source MCP server and Claude Code plugin turns LLMs into condition monitoring assistants. Engineers describe their needs in plain language, and the AI calls the right analysis tools to deliver results. It supports and accelerates expert decision-making while keeping data local.

predictive-maintenance-mcp is currently grouped under Observability, which makes it easier to evaluate through workflow fit instead of isolated features alone. Based on the available data, it leans most heavily toward 52 specialized MCP endpoints (46 tools, 2 resources, 4 prompts) for comprehensive analysis and Diagnose bearing faults by loading vibration signals and running spectral analysis. The listed license is NOASSERTION, which is useful when adoption constraints matter. It also shows measurable community traction with 52 GitHub stars.

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Code AssistantWorkflow AutomationRAG / Knowledge BaseMulti-AgentBrowser AutomationLLM InfraDev ToolingObservability

Not affiliated with Anthropic, OpenAI or Microsoft.

#anomaly-detection#asset-management#bearing-fault-diagnosis#claude-agents#claude-ai#condition-monitoring#envelope-analysis#fastmcp
$ Install
$ pip install predictive-maintenance-mcp
↗ Visit site★ GitHub
01

Features

0152 specialized MCP endpoints (46 tools, 2 resources, 4 prompts) for comprehensive analysis
02Privacy-first: raw data never leaves the local machine
03LLM-agnostic: works with Claude, GPT, Ollama, and any MCP-compatible client
04Modular architecture: use only the tools you need and extend with custom modules
05Interactive HTML and Word report generation with fault markers and severity assessment
02

Why choose it

+52 specialized MCP endpoints (46 tools, 2 resources, 4 prompts) for comprehensive analysis
+Diagnose bearing faults by loading vibration signals and running spectral analysis
+Covers 3 supported environments or platforms, which is helpful for broader deployment needs.
+Ships with a public repository and a NOASSERTION license, which makes adoption and review easier.
03

Trade-offs

!There are at least 8 related tools in the same category, so the best choice is easier to make after side-by-side comparison.
04

Compatibility

Windows
Windows
Verified via docs
macOS
macOS
Verified via docs
Linux
Linux
Verified via docs
05

Quick start

1
$ pip install predictive-maintenance-mcp
06

Use cases

↳Diagnose bearing faults by loading vibration signals and running spectral analysis
↳Generate professional diagnostic reports with charts and severity assessment
↳Train anomaly detection models on healthy baselines and flag outliers in new signals
07

How it compares

≈predictive-maintenance-mcp sits in the Observability category, so it makes more sense to evaluate it alongside tools like worldmonitor instead of in isolation.
≈If your main need is closer to "Diagnose bearing faults by loading vibration signals and running spectral analysis", that use case is a better lens for comparison than broad feature checklists alone.
≈predictive-maintenance-mcp uses a NOASSERTION license, and community traction are both easier to judge in category context.
08

Alternatives

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worldmonitor★ 62.5k
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GitHub MCP Server logo
GitHub MCP Server★ 31.6k
GitHub's official MCP Server. Allows AI agents to interact directly with your GitHub repositories (read files, search code, issues).
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fastmcp logo
fastmcp

Related searches

predictive-maintenance-mcp AlternativesBest Observability Tools 2026Open Source Observabilitypredictive-maintenance-mcp Tutorialpredictive-maintenance-mcp Vs Competitorsanomaly-detectionasset-managementbearing-fault-diagnosis

Comments

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  • ?
    usr_seed_0424May 20, 2026

    Used for industrial equipment monitoring automation, the fault detection accuracy is good

  • ?
    usr_seed_0385May 18, 2026

    AI-powered fault diagnosis via MCP brings predictive maintenance to conversational workflows

  • ?
    usr_seed_0435May 11, 2026

    The maintenance intelligence integrates with existing monitoring data sources cleanly

  • ?
    usr_seed_0369Apr 14, 2026

    Good for operations teams that want AI-assisted maintenance without custom ML pipelines

On this page
01Features02Why choose it03Trade-offs04Compatibility05Quick start06Use cases
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07
How it compares
08Alternatives
Stats
GitHub Stars★ 52
Last commit1w ago
StatusActive
LicenseNOASSERTION
CategoryObservability
Trend (30d)
+2↑ 0.7%
Links
Documentation↗Discussion↗Issues↗Releases↗

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