HRV Coherence Calculator MCP for AI. Assess Autonomic Balance from R-R Intervals

Q: How do I use the calculatermssd tool?

You provide a series of R-R intervals. Remember that calculatermssd needs at least three readings to run its short-term variability analysis.

Q: What does classifycoherencelevel do?

classifycoherencelevel takes your calculated metrics, plus your age and sex, to produce a final classification of your overall system balance.

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

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The HRV Coherence Calculator processes successive heart beat time measurements (R-R intervals) to generate key Heart Rate Variability (HRV) metrics.

It computes measures like RMSSD and SDNN, then uses these results to classify the overall coherence level of your cardiorespiratory system.

This helps assess the balance between sympathetic and parasympathetic nervous systems.

What your AI can do

Classify coherence level

Takes calculated HRV metrics and your age/sex to classify your cardiorespiratory coherence level.

Calculate rmssd

Calculates Root Mean Square of Successive Differences (RMSSD) from your R-R intervals, needing at least three data points.

Calculate sdnn

Determines the Standard Deviation of NN intervals (SDNN) using R-R intervals; it requires a minimum of five readings for reliable results.

Calculate Short-Term Variability

It determines the Root Mean Square of Successive Differences (RMSSD) from your R-R intervals.

Measure Total Variability

It calculates the Standard Deviation of NN intervals (SDNN), providing an overall view of heart rate changes.

Assess System Balance

It classifies your cardiorespiratory coherence level based on the calculated HRV metrics and your demographic data.

Ask an AI about this

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AI Agent

HRV Coherence Calculator: 3 Tools

These tools allow you to calculate specific measures of heart rate variability, from short-term changes (RMSSD) to overall system balance (Coherence Level).

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Classify Coherence Level

Takes calculated HRV metrics and your age/sex to classify your cardiorespiratory coherence level.

Calculate Rmssd

Calculates Root Mean Square of Successive Differences (RMSSD) from your R-R...

Calculate Sdnn

Determines the Standard Deviation of NN intervals (SDNN) using R-R intervals; it...

Security and governance baked right in.

Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.

Claude AI

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

Start a conversation

Open a new chat. The HRV Coherence Calculator integration is available immediately — no restart needed.

Antigravity

Configure Agent Environment

Open your Antigravity agent's workspace configuration or mcp-servers.json file.

Bind the Endpoint

Add the Vinkius endpoint URL to your agent's MCP connections list:

"mcp_servers": {
  "hrv-coherence-calculator": {
    "serverUrl": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
  }
}

Provide your secure token in place of [YOUR_TOKEN_HERE] to ensure your agent requests are authenticated.

Execute

Start your Antigravity session. The agent will autonomously discover and utilize the HRV Coherence Calculator tools with full Vinkius guardrails applied.

VS Code Copilot

⚡

One-Click Install (Recommended)

In your Vinkius Dashboard, simply click the Add to VS Code button for this server. We'll automatically configure your local workspace.

Or configure manually

Open MCP Settings

Open VS Code, press Ctrl/Cmd + Shift + P, and search for GitHub Copilot: MCP Servers.

Add Server Config

Add the Vinkius endpoint configuration to your mcp-servers.json file:

"hrv-coherence-calculator": {
  "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}

Ensure you replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.

LangChain

Install Dependencies

Install the LangChain MCP adapters for your environment:

pip install langchain-mcp-adapters

Connect the Server

Use the SSEClient in LangChain to connect to the Vinkius managed endpoint:

from langchain_mcp_adapters.client import SSEClient

# Connect to Vinkius
client = SSEClient(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
tools = client.get_tools()

CrewAI

Define the Tool

Load the Vinkius MCP tools into your CrewAI agents:

from crewai import Agent
from mcp_crewai import MCPTool

# Connect securely to Vinkius
vinkius_tools = MCPTool(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

# Assign to Agent
researcher = Agent(
    role='Data Researcher',
    tools=vinkius_tools.get_all()
)

Execute Task

Run your CrewAI process. The agent will autonomously route tasks to the Vinkius managed server.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

Import from OpenAPI, Swagger, or YAML specs
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Publish to catalog or keep private

Start building

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Start with HRV Coherence Calculator, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by HRV Coherence Calculator. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This connection provides 3 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

Interpreting raw variability numbers is tricky today.

Right now, when you look at a stream of R-R intervals, interpreting the significance is tough. You get one number for short-term change, another for total variance, and you're left trying to figure out which metric actually matters most about your body's balance.

With this MCP, you send in the raw data, and it runs all the required calculations internally. Instead of getting three separate numbers that need manual interpretation, you get a single coherence classification—a clear summary of how balanced your system is.

The HRV Coherence Calculator provides System Coherence Level.

You no longer have to manually take the outputs from `calculate_rmssd` and `calculate_sdnn` and then try to map them against a chart. The tool handles that correlation internally.

What you get is an immediate, clinically useful assessment of your cardiorespiratory state, saving time and reducing misinterpretation.

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What your AI can actually do with this

Understanding heart rate variability isn't just about looking at a number; it tells you how balanced your autonomic nervous system is. Raw R-R interval data can be tough to interpret, so this MCP steps in. It takes those successive beat timings and calculates metrics like RMSSD and SDNN, giving you specific measures of short-term and total variability.

Then, the best part: it uses all that data to classify your overall cardiorespiratory coherence level. This helps show if your cardiovascular system is operating optimally or if something needs adjustment. You can connect this MCP through Vinkius, accessing it right alongside thousands of other tools in one place, giving you a deep look into physiological health without the complexity.

Built · Hosted · Managed by Vinkius HRV Coherence Calculator - Assess Autonomic Balance

Server ID 019ec7cc-e61e-71ea-b585-ebbd685a217a

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Score 100/100

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What Changes When You Connect

Don't just measure a number. By running calculate_rmssd and calculate_sdnn, you get two distinct metrics that paint a fuller picture of your heart's activity.

Get beyond simple diagnostics. The tool uses the resulting metrics to run classify_coherence_level, giving an actionable assessment of overall system coherence, not just isolated variability.

The analysis accounts for demographics. When you use classify_coherence_level, it incorporates your age and sex, which is crucial context for interpreting HRV scores correctly.

You avoid guessing. Instead of trying to manually correlate multiple metrics, the process guides you through using all three tools sequentially for a complete report.

It handles complex data inputs. You feed in raw R-R intervals, and the MCP manages the calculations required for both time-domain and frequency-related assessments.

See it in action

01 01

Evaluating Post-Stress Recovery

A patient returns from a high-stress event. Instead of just knowing their resting heart rate, your agent runs calculate_rmssd and compares it to baseline data. A dip in RMSSD suggests the parasympathetic system hasn't recovered fully.

02 02

Tracking Training Load

An athlete wants to know if their training volume is too high. They feed in recent R-R intervals, and the MCP calculates SDNN and then uses classify_coherence_level to determine if they are overtraining or recovering optimally.

03 03

Initial Health Screening

A client needs a general health baseline. You run all three tools: first, calculating RMSSD; second, calculating SDNN; and finally, using classify_coherence_level to synthesize the data into one coherent assessment.

04 04

Comparing Longitudinal Data

A physician needs to track a patient's improvement over six months. They run all three tools at each check-in, letting the pattern of changes in RMSSD and coherence level guide treatment adjustments.

The honest tradeoffs

Looking at one metric alone

Anti-pattern

Only running calculate_rmssd gives you a short-term view, but if your total variability is low, that single number might look fine but miss the bigger picture.

The Fix

Always combine metrics. First, run both calculate_rmssd and calculate_sdnn. Then, pass those results to classify_coherence_level along with demographics for a complete assessment.

Ignoring data prerequisites

Anti-pattern

Trying to calculate metrics with too few readings. The tool will fail or give unreliable numbers if you don't provide enough R-R intervals.

The Fix

Ensure you have quality data. Remember that calculate_rmssd needs at least three recordings, and calculate_sdnn requires five for reliable stats.

Treating metrics as absolute

Anti-pattern

Assuming a low coherence score means an immediate medical crisis. Metrics are relative to your baseline and demographics.

The Fix

Always use the classify_coherence_level tool in context. Compare results against historical data or clinical guidelines, not just raw numbers.

When It Fits, When It Doesn't

Use this MCP if you need a multi-faceted assessment of autonomic nervous system balance using R-R intervals. You must run all three tools to get the full picture: calculate_rmssd for immediate short-term changes, calculate_sdnn for total variability context, and finally, classify_coherence_level to synthesize those results into a single coherence score. Don't use this if you just need a simple heart rate reading; that's better handled by a dedicated pulse monitor app. Also, don't rely on it if your input data is noisy or has movement artifacts—the results will be meaningless.

Questions you might have

How do I use the calculate_rmssd tool? +

You provide a series of R-R intervals. Remember that calculate_rmssd needs at least three readings to run its short-term variability analysis.

Do I need both RMSSD and SDNN for coherence? +

Yes, the process is designed to use multiple metrics. You should calculate both using calculate_rmssd and calculate_sdnn, then pass those values into classify_coherence_level.

What does classify_coherence_level do? +

classify_coherence_level takes your calculated metrics, plus your age and sex, to produce a final classification of your overall system balance.

Can I use this MCP with my existing EHR system? +

You can connect to the full suite of tools via Vinkius. Your AI client connects once through Vinkius and gains access to the entire catalog, allowing you to integrate these metrics into your workflow.

What specific format should my R-R interval data be for the `calculate_rmssd` tool? +

The tool requires time series data provided in milliseconds. Make sure your input array uses a consistent unit; if you mix units, the calculation will fail or produce inaccurate results.

If my dataset is small, what should I know about using `calculate_sdnn`? +

You must provide at least five readings for reliable output. The tool's calculated variability metrics significantly lose accuracy when you use fewer than five data points.

How do I ensure I get a complete coherence assessment using all three tools? +

The workflow requires calculating both RMSSD and SDNN first. You then pass those two derived metrics into the classify_coherence_level function, along with your age and sex.

What are the performance considerations or rate limits for using this MCP? +

Vinkius handles standard usage volume. If you plan to process extremely large batches of R-R data in quick succession, check our enterprise documentation regarding high-volume API access.

What is the difference between RMSSD and SDNN? +

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.

Do I need to provide age and sex? +

Yes. The classification of your coherence level requires demographic context. The classify_coherence_level tool uses your age and sex to compare calculated metrics against established physiological norms.

What are the minimum requirements for input data? +

Data constraints vary by metric. calculate_rmssd requires a minimum of 3 readings, and calculate_sdnn requires at least 5 readings to ensure statistical reliability.

Connect to your AI in seconds.

Classify coherence level

Calculate rmssd

Calculate sdnn

HRV Coherence Calculator: 3 Tools

Make your AI actually useful.

Classify Coherence Level

Calculate Rmssd

Calculate Sdnn

Security and governance baked right in.

Claude AI

Open Claude Settings

Add Custom Connector

Start a conversation

Claude Code

Open your terminal

Add the MCP Server

Start coding

Cursor

One-Click Install (Recommended)

Open Cursor Settings

Add New Server

Use in Composer

Antigravity

Configure Agent Environment

Bind the Endpoint

Execute

VS Code Copilot

One-Click Install (Recommended)

Open MCP Settings

Add Server Config

Windsurf

One-Click Install (Recommended)

Open Windsurf Settings

Add Server Endpoint

LangChain

Install Dependencies

Connect the Server

CrewAI

Define the Tool

Execute Task

Choose How to Get Started

Build Your Own

Make Your AI Do More

Works with Claude, ChatGPT, Cursor, and more

Interpreting raw variability numbers is tricky today.

The HRV Coherence Calculator provides System Coherence Level.

What your AI can actually do with this

Here's how it actually works

Who is this actually for?

What Changes When You Connect

See it in action

Evaluating Post-Stress Recovery

Tracking Training Load

Initial Health Screening

Comparing Longitudinal Data

The honest tradeoffs

Looking at one metric alone

Ignoring data prerequisites

Treating metrics as absolute

When It Fits, When It Doesn't

Questions you might have