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 evaluation of Binocular Vergence Dynamics peak convergence/divergence velocity, latency, and convergence insufficiency asthenopia.
flowchart TD
BinocularDisparity["Step Binocular Disparity Jump (4-8 deg)"] --> SOA["Midbrain Supraoculomotor Area (SOA) Burst Neurons"]
SOA --> MedialRecti["Bilateral Medial Rectus Inward Co-Activation (Convergence)"]
SOA --> LateralRecti["Bilateral Lateral Rectus Outward Relaxation (Divergence)"]
MedialRecti & LateralRecti --> VelocityRatios["Peak Velocity Kinetics & Conv/Div Ratio"]
VelocityRatios --> AsthenopiaEngine["Convergence Insufficiency (CI) & Digital Eyestrain Score"]
AsthenopiaEngine --> Agent["OpenClaw / Hermes Personal Agent Screen Distance Prescriptions"]Deterministic Math: Pure Python standard library implementation with zero external dependencies.
Oculomotor Neurophysiology Standards: Implements validated equations for step vergence velocities, main sequence ratios, and settling time constants.
Digital Eyestrain & Convergence Fatigue Shield: Sensitive detection of binocular motor fusion breakdown during intense screen reading.
Model Context Protocol (MCP): Native JSON-RPC tool integration for LLMs and autonomous agents.
Related MCP server: vHIT Vestibular Gain MCP Server
from client import OculomotorVergenceDynamicsClient
client = OculomotorVergenceDynamicsClient()
result = client.evaluate_vergence_dynamics(
step_disparity_deg=6.0,
peak_convergence_velocity_deg_s=29.5,
peak_divergence_velocity_deg_s=18.2
)
print("Conv/Div Ratio:", result["convergence_divergence_ratio"])
print("Vergence Score:", result["vergence_health_score"])
print("Classification:", result["classification"])Apache-2.0
This server cannot be deployed
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