Predictive Maintenance MCP Server
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- AlicenseAqualityAmaintenanceEnables AI assistants to analyze vibration data, detect machinery faults, and generate professional diagnostic reports through natural conversation.34100MIT
- AlicenseAqualityCmaintenanceEnables 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.2126 PyPI9MIT
- AlicenseNot gradedqualityBmaintenanceEnables machine vibration analysis from ordinary handheld video, with MCP tools for subpixel displacement measurement, measurement quality assessment, and requesting better clips.MIT
- AlicenseNot gradedqualityAmaintenanceProvides 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 npm1MIT
- AlicenseNot gradedqualityDmaintenanceEnables 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
- AlicenseNot gradedqualityBmaintenanceEnables exploration of manufacturing equipment data and analysis of failure risk, supporting monitoring, failure classification, and anomaly detection through natural language.MIT
TDQS
Scored across 38 tools
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.
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.
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.
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.