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Emotional Variability

Emotional Variability MCP for AI. Measure the swings: Quantify mood amplitude and instability.

Claude Claude
ChatGPT ChatGPT
Cursor Cursor
Gemini Gemini
Windsurf Windsurf
VS Code VS Code
JetBrains JetBrains
Vercel Vercel
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Emotional Variability Score MCP on Cursor AI Code EditorEmotional Variability Score MCP on Claude Desktop AppEmotional Variability Score MCP on OpenAI Agents SDKEmotional Variability Score MCP on Visual Studio CodeEmotional Variability Score MCP on GitHub Copilot AI AgentEmotional Variability Score MCP on Google Gemini AIEmotional Variability Score MCP on Lovable AI DevelopmentEmotional Variability Score MCP on Mistral AI AgentsEmotional Variability Score MCP on Amazon AWS Bedrock

Connect to your AI in seconds.

Emotional Variability Score lets you quantify mood swings and stability over time. It measures emotional amplitude—the distance between peak highs and deep lows—and calculates instability indices.

This helps track if emotional fluctuations are increasing or decreasing week-over-week, giving stakeholders a precise view of behavioral trends.

What your AI can do

Compare weekly volatility

Compares two consecutive weeks to determine if emotional instability is worsening or getting better.

Calculate mood amplitude

Identifies the extremes of the emotional range—the highest peak and lowest valley—experienced during a time period.

Calculate instability index

Quantifies how erratic or frequent mood swings are within a specific date range, returning an index score and pattern type.

+ 1 more capabilities included
Determine overall stability status

Generate an aggregated status and rating summarizing emotional health across the entire period.

Measure peak mood range

Identify the absolute highest and lowest points experienced during a specific time window.

Quantify erratic behavior rates

Calculate an index that measures how frequent or intense mood swings are within a set date range.

Track changes in volatility trends

Compare the emotional instability between two consecutive weeks to determine if the trend is worsening or improving.

Included with Plan

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AI Agent

Emotional Variability Score (4 Tools)

These tools let you quantify specific aspects of emotional data: instability indices, mood ranges, and week-over-week volatility comparisons.

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Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

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Compare Weekly Volatility

Compares two consecutive weeks to determine if emotional instability is worsening or getting better.

Calculate Mood Amplitude

Identifies the extremes of the emotional range—the highest peak and lowest...

Calculate Instability Index

Quantifies how erratic or frequent mood swings are within a specific date range...

Get Comprehensive Emotional Report

Generates a high-level summary of all emotional metrics for a given period...

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Claude AI

Claude AI

1

Open Claude Settings

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

2

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.

3

Start a conversation

Open a new chat. The Emotional Variability integration is available immediately — no restart needed.

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.

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Start with Emotional Variability Score, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.

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  • Works with Claude, ChatGPT, Cursor, and more
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Emotional Variability MCP server cover

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Emotional Variability Score. 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 4 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

Manual mood tracking is tedious and imprecise.

Today, assessing emotional trends means digging through multiple dashboards, cross-referencing dates, and manually calculating the difference between high points and low points. You're spending hours just trying to quantify a 'mood swing,' often relying on subjective interpretation or single metrics that miss the full picture.

With this MCP, you simply tell your agent the date range and what you want measured—whether it’s amplitude or volatility. The results are immediate: concrete scores detailing how volatile or stable the emotional data is.

Using calculate_instability_index provides a clear metric of erratic behavior.

Before, determining if someone was 'erratic' required anecdotal evidence or complex statistical modeling that took days to run. Now, you input the date range, and the tool returns an instability score instantly. You get a concrete number describing the pattern type.

It shifts the conversation from 'Do they seem moody?' to 'The index shows a 35% increase in erratic behavior over two weeks.' That's how you move the discussion forward.

What your AI can actually do with this

You can't just guess at how stable an emotional pattern is; you need metrics. This MCP provides specialized methods for charting mood changes and identifying patterns in behavioral data. Instead of sifting through charts to see if the peaks are getting bigger or the dips are getting deeper, your agent handles the math.

You can quickly quantify exactly how erratic a person's mood swings are within any given timeframe. Need to know if stability is trending up or down? You can compare weekly volatility directly and get an immediate trend line. For a full picture, you run the complete emotional report which aggregates all metrics into one summary view.

When you connect this MCP via Vinkius, your agent becomes a powerful behavioral analyst right inside your workflow.

Built · Hosted · Managed by Vinkius Emotional Variability Score - Track Mood Stability and Volatility
Server ID 019ed640-756b-719a-9bad-97f262d37f14
Vinkius Inspector
Compliance Grade A+
Score 100/100
Vinkius Inspector Badge — Score 100/100

Questions you might have

How do I use calculate_instability_index? +

You run this tool by providing a specific start and end date range. It returns an instability score and pattern type, letting you quantify how erratic the mood swings are during that week.

What's the difference between calculate_mood_amplitude and get_comprehensive_emotional_report? +

calculate_mood_amplitude only gives you the peak/valley range. The comprehensive report pulls all metrics together, giving you a full summary, stability status, and overall rating.

Can I track weekly changes with compare_weekly_volatility? +

Yes. You run this tool by specifying two consecutive weeks. It returns the delta and trend, telling you if volatility is worsening or improving compared to last week.

Do I need all four tools for an emotional report? +

No. If you just need a summary overview, get_comprehensive_emotional_report handles that. Use the other tools if you need deeper dives into specific metrics like amplitude or volatility.

What date format must I use when calling calculate_instability_index? +

You must provide dates in YYYY-MM-DD format. The tool requires a clear start and end date to map the correct emotional period for analysis.

What happens if my dataset is incomplete when using calculate_mood_amplitude? +

If data is missing, the tool returns null values for the affected metrics. Always check your source data first; it's best to fill in gaps before running amplitude checks.

Are there limitations if I run get_comprehensive_emotional_report frequently? +

While we don't impose strict limits, frequent large requests can hit rate caps. Break down your analysis into smaller reporting chunks to keep things running smoothly.

What kind of scores are needed for compare_weekly_volatility? +

The tool requires standardized numerical scores from both weeks being compared. Ensure all emotional metrics you use have the same scale and data type.

What does the instability index measure? +

The calculate_instability_index tool measures how frequently your mood changes direction, providing a score that represents the frequency of transitions between different emotional states.

How is amplitude calculated? +

The calculate_mood_amplitude tool calculates the vertical distance between the highest (peak) and lowest (valley) mood scores recorded during your specified timeframe.

Can I compare two different weeks? +

Yes, use the compare_weekly_volatility tool by providing the start dates for both the current and previous weeks to see if your emotional stability is trending up or down.

Built & Managed by Vinkius 30s setup 4 tools

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All 4 tools are live and waiting. You're up and running in seconds.

Vinkius runs on Claude Claude
Vinkius runs on ChatGPT ChatGPT
Vinkius runs on Cursor Cursor
Vinkius runs on Gemini Gemini
Vinkius runs on Windsurf Windsurf
Vinkius runs on VS Code VS Code
Vinkius runs on JetBrains JetBrains
Vinkius runs on Vercel Vercel
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