A
licenseNot graded
qualityB
maintenanceEnables agents to compute deterministic constriction kinetics, autonomic tone, and alertness metrics from pupillometry inputs through native JSON-RPC tool integration.
7
MIT
Visual Contrast Sensitivity (Pelli-Robson) MCP Server
by Alpha-Park
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.
Visual Contrast Sensitivity (Pelli-Robson) Skill
Zero-dependency sovereign personal agent skill for non-invasive evaluation of Visual Contrast Sensitivity Function (CSF), Pelli-Robson chart log scores, and digital display ergonomics.
flowchart TD
LetterTriplets["Pelli-Robson Chart Letter Triplets (1 m distance)"] --> LogScore["Log Contrast Sensitivity Score (0.00 - 2.25)"]
LogScore --> WeberFormula["Weber Physical Contrast Percentage (10^-log * 100)"]
WeberFormula --> AcuityEngine["Visual Contrast Acuity & Glare Resilience Engine"]
AcuityEngine --> Agent["OpenClaw / Hermes Personal Agent Actions"]Features
Deterministic Math: Pure Python standard library implementation with zero external dependencies.
Clinical Psychophysical Standards: Implements validated logarithmic Pelli-Robson formulas.
Display Readability & Glare Stratification: Direct quantification of visual edge sharpness under digital screen work.
Model Context Protocol (MCP): Native JSON-RPC tool integration for LLMs and autonomous agents.
Related MCP server: Quantitative Pupillary Light Reflex (PLR) Skill
Quickstart
from client import OcularContrastSensitivityClient
client = OcularContrastSensitivityClient()
result = client.evaluate_contrast_sensitivity(
log_contrast_score=1.80
)
print("Weber Contrast Threshold:", result["weber_contrast_threshold_pct"], "%")
print("Classification:", result["classification"])License
Apache-2.0
This server cannot be deployed
Maintenance
ActivityMaintained
ResponsivenessNo issues
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