Provides quantitative pupillary light reflex metrics and autonomic tone assessments to Hermes/OpenClaw personal agents for use in agent actions.
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 calculation of Pupillary Light Reflex (PLR) dynamics, autonomic tone, and central nervous system alertness.
flowchart TD
PupilCam["Infrared Pupillometry / Optical Sensor"] --> Dynamics["Pupil Diameter Tracking Over Time"]
Dynamics --> Extraction["Kinetic Metric Extraction (Latency, MCV, %CA, RDV)"]
Extraction --> ScoreEngine["Autonomic Pupillary Resilience Engine"]
ScoreEngine --> Agent["OpenClaw / Hermes Personal Agent Actions"]Deterministic Math: Pure Python standard library implementation with zero external dependencies.
Pupillometric Standard Metrics: Implements validated equations for constriction velocity, amplitude percentage, and redilation rate.
Autonomic Tone Assessment: Quantifies balance between cholinergic parasympathetic and adrenergic sympathetic branches.
Model Context Protocol (MCP): Native JSON-RPC tool integration for LLMs and autonomous agents.
Related MCP server: Auditory Binaural Masking Level Difference (BMLD) MCP Server
from client import PupilLightReflexClient
client = PupilLightReflexClient()
result = client.evaluate_pupillary_dynamics(
baseline_diameter_mm=4.3,
minimum_diameter_mm=2.6,
constriction_latency_ms=225.0,
constriction_velocity_mm_s=4.9,
redilation_velocity_mm_s=1.85
)
print("Constriction Amplitude:", result["constriction_amplitude_mm"], "mm")
print("Classification:", result["classification"])Apache-2.0
This server cannot be deployed
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