This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
Zero-dependency sovereign personal agent skill for non-invasive analysis of Muscle Mechanomyography (MMG), electromechanical delay (EMD), and skeletal muscle fatigue kinetics.
flowchart TD
AcousticSensor["Piezoelectric / Accelerometer MMG Sensors"] --> Vibration["Lateral Intramuscular Pressure Waves"]
Vibration --> Spectral["Time-Frequency Processing (RMS, MPF, EMD)"]
Spectral --> ContractileScore["Muscular Contractile Dynamic Engine"]
ContractileScore --> Agent["OpenClaw / Hermes Personal Agent Actions"]Deterministic Math: Pure Python standard library implementation with zero external dependencies.
Biomechanical Standard Metrics: Implements validated equations for mechanomyographic amplitude and spectral power frequency.
Peripheral Muscular Fatigue Stratification: Quantitative evaluation of excitation-contraction uncoupling and cross-bridge kinetics.
Model Context Protocol (MCP): Native JSON-RPC tool integration for LLMs and autonomous agents.
Related MCP server: Cardiac Systolic Time Intervals (PEP/LVET) & Inotropy Skill
from client import MuscleMechanomyographyClient
client = MuscleMechanomyographyClient()
result = client.evaluate_mechanomyography(
mmg_rms_amplitude_mv=1.92,
mmg_mean_power_frequency_hz=27.4,
electromechanical_delay_ms=33.5
)
print("Contractile Score:", result["muscular_contractile_score"])
print("Classification:", result["classification"])Apache-2.0
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