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 Hypercapnic Ventilatory Response (HCVR slope), central respiratory chemosensitivity, and CO2 tolerance.
flowchart TD
Capnometry["End-Tidal CO2 & Spirometry Sensors"] --> LinearReg["Linear Regression Slope (dVE / dPETCO2)"]
LinearReg --> LoopGain["Chemoreflex Loop Gain & Threshold Analysis"]
LoopGain --> PacingEngine["Respiratory Chemoreflex Stability Engine"]
PacingEngine --> Agent["OpenClaw / Hermes Personal Agent Actions"]Deterministic Math: Pure Python standard library implementation with zero external dependencies.
Respiratory Physiology Standards: Implements validated Read rebreathing linear sensitivity slope calculations.
Chemoreflex Loop Gain Stratification: Quantitative evaluation of central controller sensitivity and periodic breathing vulnerability.
Model Context Protocol (MCP): Native JSON-RPC tool integration for LLMs and autonomous agents.
Related MCP server: salivary-alpha-amylase
from client import RespiratoryChemoreflexClient
client = RespiratoryChemoreflexClient()
result = client.evaluate_hypercapnic_response(
delta_petco2_mmhg=11.0,
delta_minute_ventilation_l_min=25.3
)
print("HCVR Slope:", result["hcvr_slope_l_min_mmhg"])
print("Classification:", result["classification"])Apache-2.0
This server cannot be deployed
Cardiopulmonary exercise test (CPET) analysis: ventilatory thresholds, metrics and substrate use.
Deterministic fitness coaching engine: adaptive programs, progression math, readiness autoregulation
Fuses biometric signals into a stress score (0-100) for AI adaptation. MCP + A2A native.
The adaptive health-messaging engine for apps and agents. Federally-sourced. Not medical advice.
