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 Electroretinogram Photopic Negative Response (PhNR), retinal ganglion cell health, and ocular strain.
flowchart TD
PhotopicFlash["Photopic Light Stimulus"] --> ERG_Waveform["Full-Field Electroretinogram Recording"]
ERG_Waveform --> FeatureExtract["Extraction: a-wave, b-wave, PhNR Trough & Latency"]
FeatureExtract --> RatioCalc["PhNR/b-wave Ratio & Retinal Ganglion Engine"]
RatioCalc --> Agent["OpenClaw / Hermes Personal Agent Actions"]Deterministic Math: Pure Python standard library implementation with zero external dependencies.
ISCEV Standard Electrophysiology: Implements clinical electroretinography standards for photopic negative response quantification.
Retinal Ganglion Strain Stratification: Quantitative detection of inner retinal stress before irreversible visual loss.
Model Context Protocol (MCP): Native JSON-RPC tool integration for LLMs and autonomous agents.
Related MCP server: ocular-tear-film
from client import ElectroretinogramPhNRClient
client = ElectroretinogramPhNRClient()
result = client.evaluate_phnr(
b_wave_amplitude_uv=118.0,
phnr_amplitude_uv=46.5,
phnr_latency_ms=71.5
)
print("PhNR/b-wave Ratio:", result["phnr_b_wave_ratio"])
print("Classification:", result["classification"])Apache-2.0
This server cannot be deployed
Fuses biometric signals into a stress score (0-100) for AI adaptation. MCP + A2A native.
Physician-reviewed medical opinions and prescriptions for AI agents.
Medical RAG: semantic search for clinical guidelines, drug interactions, diagnoses & EHR data.
Medical RAG: semantic search for clinical guidelines, drug interactions, diagnoses & EHR data.
