Skip to main content
Glama
lucksmiler-A1

Predictive Maintenance MCP Server

Related Servers

Alternatives to Predictive Maintenance MCP Server

No user-submitted related servers found.

    Related Servers

    • A
      license
      A
      quality
      C
      maintenance
      Enables predictive maintenance for electric motors by analyzing stator current signals to detect faults like broken rotor bars, bearing defects, and eccentricity, using spectral and envelope analysis techniques.
      21
      26 PyPI
      9
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables machine vibration analysis from ordinary handheld video, with MCP tools for subpixel displacement measurement, measurement quality assessment, and requesting better clips.
      MIT
    • A
      license
      Not graded
      quality
      A
      maintenance
      Provides AI assistants with local Wi-Fi diagnostics including connection history analysis, live signal sampling, and connectivity diagnosis. It returns findings and verdicts rather than raw data, and runs on Windows, Linux, and macOS without sending data off the machine.
      46 npm
      1
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      Enables natural language analysis of mechanical test data files (CSV, TDMS, MDF) by providing tools for channel statistics, spectrum analysis, rainflow fatigue counting, thermal state detection, and report generation.
      MIT

    TDQS

    A4.3/5.0

    Scored across 38 tools

    Disambiguation4/5

    Most tools have clearly distinct purposes and the descriptions repeatedly mark 'THE unified' tool to prevent overlap (analyze_envelope, assess_severity, analyze_signal_trend, check_bearing_faults). A few pairs remain confusable: analyze_statistics vs extract_features_from_signal (both time-domain feature extraction), analyze_fft vs compute_power_spectral_density, and the large cluster of generate_*_report tools plus plot_signal where selection depends on output artifact rather than action.

    Naming Consistency5/5

    Consistent snake_case verb_noun pattern throughout (load_signal, analyze_fft, generate_envelope_report, declare_measurement_point, assess_asset_change). Verbs are varied but semantically appropriate and prefix-grouped by sub-domain, with no mixed conventions. The only minor deviation is the generate_diagnostic_report vs generate_diagnostic_report_docx suffix pair, which is still readable.

    Tool Count2/5

    38 tools is well above the heavy threshold for a single server, spanning seven-plus sub-domains (signal IO, spectral/statistical analysis, ML anomaly detection, manuals/catalog, ledger/baselines, prognosis, reporting). Several report generators (fft/envelope/iso/pca/feature-comparison/diagnostic/docx) plausibly consolidate, making the surface larger than the core scope requires.

    Completeness5/5

    Coverage is unusually complete: signal load/list/clear, statistical and spectral analysis, bearing fault detection, ISO severity, ML training/prediction, RUL and trend prognostics, manual/catalog search, an append-only asset ledger with baseline declarations and change assessment, plus multi-format reporting. Lifecycle and CRUD operations across the domain are well represented with no obvious dead ends.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues