A
licenseA
qualityC
maintenanceEnables AI assistants to load vibration signals, detect machinery faults with spectral evidence, assess severity against ISO 20816-3, and generate diagnostic reports locally without guessing.
38
MIT
mcp-server-mcsa
by LGDiMaggio
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TDQS
A4/5.0
Scored across 21 tools
Disambiguation5/5
Every tool has a clearly distinct purpose. Spectrum-related tools are differentiated by method (FFT, Welch, envelope, STFT). Fault detection tools are separated by fault type (bearing, rotor bar, eccentricity, stator). Workflow tools like run_full_diagnosis and diagnose_from_file are distinct in input method. No ambiguity.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., compute_spectrum, detect_broken_rotor_bars). The verbs are descriptive and uniform, making the set predictable and easy to navigate.
Tool Count4/5
With 21 tools, the set is slightly above the typical 3-15 range but still reasonable given the comprehensive domain coverage. Each tool serves a specific, necessary function in the MCSA pipeline, so the count feels appropriate rather than bloated.
Completeness5/5
The tool set covers the entire MCSA workflow: file inspection, loading, preprocessing, multiple spectrum computation methods, fault-specific detection, and automated full-diagnosis pipelines. Utilities for data management (list/clear stored data) and test signal generation are included, leaving no obvious gaps.
Maintenance
ActivityInactive
ResponsivenessUnresponsive