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 Signal-Averaged ECG (SAECG) Ventricular Late Potentials (VLPs), terminal microvolt fragmentation, and arrhythmogenic substrate screening.
flowchart TD
RawECG["High-Resolution Multilead Signal-Averaged ECG (>= 250 beats)"] --> BiFilter["Bi-Directional 40 Hz High-Pass Butterworth Filter"]
BiFilter --> VectorMag["Vector Magnitude QRS Complex: X^2 + Y^2 + Z^2"]
VectorMag --> SimsonMetrics["Simson Criteria: Filtered QRS (fQRS), RMS40 (uV), LAS40 (ms)"]
SimsonMetrics --> VLP_Check["Late Potentials Gating (>= 2 of 3 Criteria Met)"]
VLP_Check --> SynchronyScore["Myocardial Conduction Synchrony & Arrhythmic Risk Score"]
SynchronyScore --> Agent["OpenClaw / Hermes Personal Agent Recovery Guarding"]Deterministic Math: Pure Python standard library implementation with zero external dependencies.
Electrophysiology Standards: Implements validated Simson 40 Hz high-pass criteria for ventricular late potentials.
Myocardial Electrical Stability: Highly sensitive detection of slow, fragmented conduction predisposing to re-entrant ventricular tachyarrhythmias.
Model Context Protocol (MCP): Native JSON-RPC tool integration for LLMs and autonomous agents.
Related MCP server: Electroretinogram Photopic Negative Response (PhNR) Skill
from client import CardiacVentricularLatePotentialsClient
client = CardiacVentricularLatePotentialsClient()
result = client.evaluate_saecg(
filtered_qrs_duration_ms=98.5,
rms_40_uv=38.2,
las_40_ms=24.0
)
print("VLP Positive:", result["vlp_positive"])
print("Synchrony Score:", result["myocardial_synchrony_score"])
print("Classification:", result["classification"])Apache-2.0
This server cannot be deployed
Workflow diagnostics, capability routing, and x402 settlement for MCP-compatible agents.
Market regime, execution-cost, bar-QC and backtest-audit tools for agents. Pay per call via x402.
A paid remote MCP for SREGym, built to return verdicts, receipts, usage logs, and audit-ready JSON.
Independent statistical feature evaluation for machine learning and forecasting. ML Referee tests up to 1,000 candidate features in one run against a declared baseline, helping researchers and AI agents decide which inputs to pursue, which bounded feature repairs merit further testing, and where more evidence is needed—without a separate ablation run for every feature. Use the MCP tools to discover input schemas, check access and credits, preflight a dataset, obtain a quote, submit an authorized evaluation, and retrieve structured results and reports. Results are scoped to the submitted evidence and evaluation design; they do not guarantee future model performance. Invite-only beta with manual approval. Public tool discovery requires no authentication; account and evaluation tools require OAuth through approved client connections and available evaluation credits. Connect directly to https://api.mlreferee.com/mcp. Website: https://mlreferee.com Request beta access: https://mlreferee.com/beta Agent setup and documentation: https://mlreferee.com/agents