Emotional Variability MCP for AI. Measure the swings: Quantify mood amplitude and instability.
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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.
Generate an aggregated status and rating summarizing emotional health across the entire period.
Identify the absolute highest and lowest points experienced during a specific time window.
Calculate an index that measures how frequent or intense mood swings are within a set date range.
Compare the emotional instability between two consecutive weeks to determine if the trend is worsening or improving.
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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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Start using Emotional Variability Score on VinkiusCompare 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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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.
019ed640-756b-719a-9bad-97f262d37f14 Here's how it actually works
The bottom line is that you get quantitative evidence about mood swings, not just subjective feelings.
You tell your AI client the specific time frame you need evaluated, like 'October 1st through October 31st'.
Your agent then selects and runs the appropriate tool—for example, to get a summary report or compare two weeks of data.
The MCP returns structured metrics, giving you concrete scores for instability, amplitude range, and stability status.
Who is this actually for?
Behavioral health analysts, risk management teams, or performance coaches who need to track subtle shifts in human emotional data. You're the person staring at dashboards at 2 AM, trying to figure out if a dip last month was normal or a warning sign.
Determines if recent changes in mood amplitude are statistically significant and tracks long-term stability patterns for longitudinal studies.
Models potential risk exposure by analyzing the rate of emotional variability to flag high-risk periods before operational failure occurs.
Uses weekly volatility comparisons to advise clients on improving consistency and reducing the frequency of sharp mood swings.
What Changes When You Connect
Pinpoint when stability is slipping. Use compare_weekly_volatility to get a trend showing if emotional volatility is increasing or decreasing week-over-week.
Understand the scope of mood dips. Running calculate_mood_amplitude reveals the absolute peak value, valley value, and total range for any given period.
Pinpoint erratic behavior rates. The calculate_instability_index tool quantifies how frequently or sharply mood swings happen within a specific date window.
Get an immediate overview. Instead of running four different reports, use get_comprehensive_emotional_report to pull together stability status, range, and rating in one shot.
Identify the source of risk. By pairing calculate_instability_index with compare_weekly_volatility, you can see if high instability is leading to worsening volatility.
See it in action
Tracking Client Progress
A coach needs to know if a client's emotional management techniques are working. Instead of subjective feedback, the agent runs compare_weekly_volatility and compares two months of data. The resulting delta shows a clear trend toward lower volatility, providing measurable proof of improvement.
Incident Review
A team needs to review performance during a known crisis period. They run get_comprehensive_emotional_report for the full month and then use calculate_mood_amplitude to isolate exactly how deep the lowest point was, helping pinpoint when support failed.
Identifying Behavioral Triggers
A researcher wants to know if a specific event caused mood swings. They run calculate_instability_index for the period before and after the event. A sharp spike in the index pinpoints the potential trigger with quantitative evidence.
Board Reporting
Executives need a quick status check without digging into raw numbers. They ask the agent to run get_comprehensive_emotional_report for Q2, getting an immediate stability rating and summary of all key metrics in one actionable view.
The honest tradeoffs
Treating mood as a single metric
Just running calculate_instability_index and assuming that score tells you everything about the period's emotional health.
Don't rely on one number. Pair calculate_instability_index with compare_weekly_volatility to see if the instability is a new problem or an old trend worsening over time.
Ignoring range data
Only checking for stability using get_comprehensive_emotional_report and missing the depth of emotional swings.
Always check calculate_mood_amplitude to understand the total scale. Knowing the peak and valley gives context that a simple 'stable' rating can't provide.
Comparing apples to oranges
Using compare_weekly_volatility when you only need a snapshot of one week.
If you just want the status for one specific period, run get_comprehensive_emotional_report. Only use compare_weekly_volatility when comparing two adjacent periods.
When It Fits, When It Doesn't
Use this MCP if your primary concern is measuring behavioral risk or tracking emotional consistency over time. This toolset excels at quantifying the difference between stable, predictable patterns and highly erratic fluctuations. Don't use it if you need to measure physical metrics, operational failure rates, or financial liquidity; those require entirely different types of data sources. If you simply want a single score without context, that’s insufficient—you need the detail provided by running calculate_mood_amplitude alongside get_comprehensive_emotional_report. If your goal is purely forecasting based on external market signals, look for specialized predictive modeling tools instead.
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.
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