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LGDiMaggio

Predictive Maintenance MCP Server

by LGDiMaggio
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Python
Hybrid

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    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A4.1/5.0

    Scored across 34 tools

    Disambiguation3/5

    Many tools are clearly separated by resource or analysis type, and the descriptions are detailed. However, several boundaries are fuzzy—diagnose_vibration and generate_diagnostic_report expose nearly the same integrated pipeline, and the frequency/report cluster (analyze_fft, compute_psd, compute_spectrogram_stft, generate_fft_report) requires careful reading to choose correctly.

    Naming Consistency4/5

    Names consistently follow lower_snake_case verb_noun and are broadly predictable: list_*, load_*, generate_*_report, analyze_*. Minor deviations—plot_signal versus the generate_*_report family, compute_* versus analyze_* for related analyses, and list_html_reports actually listing all report types—keep it from being perfectly uniform.

    Tool Count2/5

    34 tools is well above the 25+ threshold for a coherent set and will burden an agent with selection overhead. Many report generators could be consolidated, and the frequency-analysis/report cluster overlaps enough that the same underlying capability is exposed through several different tools.

    Completeness4/5

    The server covers the full predictive-maintenance workflow: signal generation/loading/inspection, time- and frequency-domain analysis, bearing checks, ISO severity, anomaly detection, integrated diagnostic reports, and RUL estimation. Gaps are minor—no model listing/deletion, no report deletion, no raw signal export—but the main load→analyze→diagnose→report flow has no dead ends.

    Maintenance

    ActivityActive
    ResponsivenessSlow

    Resources