mcp-server-mcsa

A Model Context Protocol (MCP) server for Motor Current Signature Analysis (MCSA) — non-invasive spectral analysis and fault detection in electric motors using stator-current signals.

mcp-server-mcsa turns any LLM into a predictive-maintenance expert. By integrating advanced techniques such as Fast Fourier Transform (FFT) and envelope analysis, the system can listen to a motor's electrical signature and automatically identify mechanical and electrical anomalies — all through natural language.

MCSA is an industry-standard condition-monitoring technique that analyses the harmonic content of the stator current to detect rotor, stator, bearing, and air-gap faults in electric motors — without requiring vibration sensors, downtime, or physical access to the machine. This server brings the full MCSA diagnostic workflow to any MCP-compatible AI assistant (Claude Desktop, VS Code Copilot, and others), enabling both interactive expert analysis and automated condition-monitoring pipelines.

Features

• Real signal loading — read measured data from CSV, TSV, WAV, and NumPy .npy files
• Motor parameter calculation — slip, synchronous speed, rotor frequency from nameplate data
• Fault frequency computation — broken rotor bars, eccentricity, stator faults, mixed eccentricity
• Bearing defect frequencies — BPFO, BPFI, BSF, FTF from bearing geometry
• Signal preprocessing — DC removal, normalisation, windowing, bandpass/notch filtering
• Spectral analysis — FFT spectrum, Welch PSD, spectral peak detection
• Envelope analysis — Hilbert-transform demodulation for mechanical/bearing faults
• Time-frequency analysis — STFT with frequency tracking for non-stationary conditions
• Fault detection — automated severity classification (healthy / incipient / moderate / severe)
• One-shot diagnostics — full pipeline from signal array or directly from file
• Test signal generation — synthetic signals with configurable fault injection for demos and benchmarking
• Persistent data store — signals and spectra saved to ~/.mcsa_data/ as compressed .npz files; referenced by short IDs (sig_xxxx, spec_xxxx) to keep large arrays out of the chat context; data survives server restarts

Tools (21)

Resources

Prompts

Installation & Setup

Step 1 — Install uv (one-time, if you don't have it)

uv is the recommended Python package manager. It handles everything (Python, packages, virtual environments) in a single tool and is used throughout the MCP ecosystem.

Windows (PowerShell):

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

macOS / Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

After installing, restart your terminal so the uv / uvx commands are available.

Step 2 — Verify it works

uvx mcp-server-mcsa --help

You should see the help text. That's it — no pip install needed. uvx downloads and runs the package automatically in an isolated environment.

Step 3 — Add to your MCP client

Pick your client and add the configuration below. No other steps are required.

Claude Desktop

Open the config file:

• Windows: %APPDATA%\Claude\claude_desktop_config.json
• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

Add mcsa inside the mcpServers object (create the file if it doesn't exist):

{
  "mcpServers": {
    "mcsa": {
      "command": "uvx",
      "args": ["mcp-server-mcsa"]
    }
  }
}

Then restart Claude Desktop.

VS Code (Copilot / Continue)

Create (or edit) .vscode/mcp.json in your workspace:

{
  "servers": {
    "mcsa": {
      "command": "uvx",
      "args": ["mcp-server-mcsa"]
    }
  }
}

Cursor

Go to Settings → MCP Servers → Add new server:

• Type: command
• Command: uvx mcp-server-mcsa

Step 4 — Test

In your MCP client, try:

"Generate a test signal with a broken rotor bar fault and run a full diagnosis. Motor: 4 poles, 50 Hz, 1470 RPM."

If the server responds with a diagnostic report, you're all set.

pip install mcp-server-mcsa

{
  "mcpServers": {
    "mcsa": {
      "command": "python",
      "args": ["-m", "mcp_server_mcsa"]
    }
  }
}

git clone https://github.com/LGDiMaggio/mcp-motor-current-signature-analysis.git
cd mcp-motor-current-signature-analysis
uv sync --dev

{
  "mcpServers": {
    "mcsa": {
      "command": "uv",
      "args": ["--directory", "/absolute/path/to/mcp-motor-current-signature-analysis", "run", "mcp-server-mcsa"]
    }
  }
}

uv run pytest

uv run mcp dev src/mcp_server_mcsa/server.py

Troubleshooting

Data Store

Signals and spectra are persisted to disk as compressed .npz files in ~/.mcsa_data/ (configurable via the MCSA_DATA_DIR environment variable). This means:

• Large arrays never enter the chat — only short IDs (sig_xxxx, spec_xxxx) and compact summaries are returned to the LLM.
• Data survives server restarts — reopen Claude Desktop tomorrow and your signals are still there.
• All data in one place — loaded measurements and generated test signals live side by side in the same folder.

~/.mcsa_data/
  signals/
    sig_a1b2c3d4.npz   ← loaded from CSV
    sig_e5f6g7h8.npz   ← generated test signal
  spectra/
    spec_i9j0k1l2.npz  ← FFT result

Use list_stored_data to see everything on disk and clear_stored_data to remove items.

Usage Examples

Real Signal — One-Shot Diagnosis

The fastest way to analyse a measured signal is the diagnose_from_file tool. Simply provide the file path and motor nameplate data:

"Diagnose the motor from C:\data\motor_phaseA.csv — 50 Hz supply, 4 poles, 1470 RPM"