HRV Coherence Calculator

HRV Coherence Calculator MCP Connector for Claude

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Calculates key Heart Rate Variability (HRV) metrics (RMSSD, SDNN) to classify cardiorespiratory coherence level using R-R intervals.

3 tools Official Updated Jun 28, 2026 Official Vinkius Partner

The Problem: Understanding heart rate variability (HRV) is critical for assessing the body's autonomic nervous system balance. Raw measurements of successive R-R intervals can be complex to interpret, especially when determining if the overall cardiovascular system coherence is optimal or reduced.

hrvheart-rate-variabilitycoherenceautonomic-nervous-systemcardiology

3 tools expose this connector's capabilities to your AI agent.

classify_coherence_level

Classify cardiorespiratory coherence level based on HRV metrics

calculate_rmssd

Requires at least 3 readings. Calculate RMSSD from R-R intervals

calculate_sdnn

Requires at least 5 readings for statistical reliability. Calculate SDNN from R-R intervals

See how to talk to your AI agent using HRV Coherence Calculator.

I have R-R intervals: [1000, 950, 1020, 1100]. My age is 30 and I am Male. Please calculate my coherence.

First, I will run `calculate_rmssd` to get the short-term measure. Then, I'll use the resulting RMSSD and any available SDNN value with `classify_coherence_level`, providing my age (30) and sex (Male), to give a full coherence assessment.

Calculate the overall variability for this long set of R-R data: [1200, 1150, 1300, 1400, 1280].

I recommend using the `calculate_sdnn` tool for this overall variability measure. Once you have both SDNN and RMSSD, remember to pass them together into the `classify_coherence_level` function along with your demographics.

I need a full coherence report. My age is 45 and I am Female. The metrics are RMSSD: 35ms and SDNN: 80ms.

Running the `classify_coherence_level` tool with your provided data (RMSSD=35, SDNN=80, Age=45, Sex=Female) will generate a detailed report on your cardiorespiratory coherence level.

RMSSD measures short-term variation (parasympathetic activity), while SDNN assesses total variability. Both metrics are calculated using dedicated tools: `calculate_rmssd` for the former and `calculate_sdnn` for the latter.

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