Mood Pattern Detector MCP for AI. Find what patterns move your emotional baseline.
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Mood Pattern Detector identifies recurring emotional trends and correlates them with lifestyle data. It analyzes historical logs to uncover cyclical patterns—for instance, pinpointing days of the week linked to lower moods.
You can measure how variables like sleep or exercise specifically influence your emotional state, and it flags dates where your mood significantly deviates from your established baseline.
What your AI can do
Analyze weekly cycle
Checks your logs to see if certain days of the week consistently correlate with better or worse moods.
Evaluate activity impact
Measures exactly how a specific variable, like sleep duration or exercise time, influences your mood levels.
Identify mood anomalies
Flags dates where your reported mood was significantly different from the average trend for that period.
Determine which days or times of the week consistently correlate with specific emotional states.
Quantify how changes in recorded activities, like sleep duration or workout intensity, affect your average mood score.
Automatically identify specific dates where your emotional state was significantly different from your historical norm.
Uncover repeating behavioral or emotional cycles over time, such as a recurring 'Sunday Scaries' pattern.
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Mood Pattern Detector: 3 Tools for Analysis
These tools allow you to investigate mood fluctuations by analyzing weekly cycles, measuring activity impacts, and flagging significant emotional anomalies in your history.
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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.
Start using Mood Pattern Detector on VinkiusAnalyze Weekly Cycle
Checks your logs to see if certain days of the week consistently correlate with better or worse moods.
Evaluate Activity Impact
Measures exactly how a specific variable, like sleep duration or exercise time...
Identify Mood Anomalies
Flags dates where your reported mood was significantly different from the average...
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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.
The Old Way: Tracking Mood and Habits
Today, tracking emotional trends means keeping detailed journals. You manually log every mood score, noting down what you ate or how many hours you slept. When you want to find a pattern—like realizing that skipping gym day makes the following week harder—you have to build complex cross-tabulations in a spreadsheet, spending hours connecting dots that might just be coincidence.
With this MCP, you feed your logs once. The system handles the heavy lifting of correlation math. It processes all that raw data and spits out actionable insights: 'Low sleep on Tuesday drops average mood by 15 points.' You get specific evidence, not just a feeling.
How Mood Pattern Detector Provides Clarity
You eliminate the need for manual trend spotting. Instead of cross-referencing sleep data with mood logs across months, you run `analyze_weekly_cycle` and immediately get to a clear answer: 'Yes, your average score is consistently lowest between 3 PM and 5 PM.'
The system gives you quantified evidence. You don't just know you feel bad; you know *why*—you know which variable failed. It turns vague feelings into testable data points.
What your AI can actually do with this
This MCP reads through your historical mood records, looking for deep, reliable patterns that are hard to spot manually. Instead of just seeing a dip in mood on a Tuesday, the detector can tell you why—it might be linked to poor sleep two nights prior or a specific type of activity.
It doesn't guess; it calculates correlations between your emotional state and external factors. You use this connector through Vinkius to get clear insights into what actually moves the needle for your well-being. The system pinpoints daily cycles, measures how exercise affects mood levels, and flags any significant deviations from your normal patterns, giving you a clearer picture of your personal rhythms.
019ed645-49cb-71d3-b884-eb582a5dce42 Here's how it actually works
The bottom line is you get structured data showing which specific lifestyle changes cause measurable shifts in your mood scores.
Connect your historical mood logs and lifestyle data sources to the MCP.
Run the analysis tool to calculate correlations between recorded variables (e.g., sleep score, exercise minutes) and your reported moods.
Review the output reports that identify patterns, measure impacts, or flag anomalies for immediate review.
Who is this actually for?
This is for biohackers, personal performance coaches, and mental health analysts. It's built for the person who spends hours cross-referencing journal entries with fitness tracker data, tired of finding patterns in spreadsheets.
Uses this MCP to analyze large sets of longitudinal patient data, identifying correlations between sleep quality and reported depressive episodes.
Checks activity impact by running evaluate_activity_impact on client logs to recommend precise lifestyle adjustments for better mood stability.
Runs pattern detection across cohorts of users, using analyze_weekly_cycle to find common emotional dips related to external life events.
What Changes When You Connect
Stop guessing about mood triggers. evaluate_activity_impact tells you, with data, whether adding 30 minutes of cardio actually improves your average daily score.
Spot predictable dips. The analyze_weekly_cycle tool flags recurring patterns—like the 'Sunday Slump'—allowing you to prep for them before they happen.
Catch unusual events immediately. Use identify_mood_anomalies when a major life event or sudden change in routine causes your mood to spike or drop unexpectedly.
Go beyond simple metrics. Instead of just seeing low moods, this MCP connects those lows to potential culprits, like poor sleep cycles.
Reduce manual data crunching. You don't have to build complex spreadsheets; the detector handles the correlation math for you.
See it in action
The inconsistent energy levels
A user notices they feel low every Friday afternoon, but can't prove why. They run analyze_weekly_cycle, which confirms a measurable dip starting Thursday, suggesting the issue isn't just 'Friday fatigue'.
Optimizing workout routines
A personal trainer needs to know if running or lifting weights has a greater mood impact. They use evaluate_activity_impact and find that strength training yields significantly better scores than long-distance cardio.
Investigating sudden burnout
After weeks of feeling 'off,' the user runs identify_mood_anomalies. The tool flags a cluster of low scores starting two months ago, pointing to a specific life stressor they hadn't connected to their mood.
The honest tradeoffs
Treating data as random
Assuming that because you felt bad last week, it means something is fundamentally wrong with your routine. This leads to unnecessary drastic changes.
Use the detector tools. First, run identify_mood_anomalies to pinpoint when the deviation started. Then use evaluate_activity_impact to test specific variables before making major life changes.
Over-relying on correlation
Seeing that sleep and mood are linked, but failing to realize that a third variable (like diet) is actually causing both the poor sleep and the low mood.
The MCP helps find patterns. Always test potential variables systematically. For instance, check if your current activity level makes sense with the weekly cycle using analyze_weekly_cycle first.
Ignoring historical context
Only looking at mood logs from the last two weeks and assuming those trends apply to years of data. This gives a skewed picture.
Ensure your source data is comprehensive. The detector needs enough history to find reliable patterns, not just isolated spikes or dips.
When It Fits, When It Doesn't
Use this MCP if your goal is pattern detection: determining why a mood change happened (Was it the week? Was it lack of sleep? Was it that specific activity?). Don't use it if you just need simple data logging; that requires basic manual entry. Also, don't use it to diagnose medical conditions—it shows correlations only. If your problem is simply 'I feel bad,' this won't help. You need a hypothesis (e.g., 'My mood drops when I sleep less than 6 hours'). Then, you run the specific tool, like evaluate_activity_impact, to test that hypothesis against your data.
Questions you might have
How does the analyze_weekly_cycle tool work? +
The analyze_weekly_cycle tool scans your historical records to determine if specific days of the week are statistically associated with consistently higher or lower mood scores across your data.
Can I use evaluate_activity_impact for anything besides exercise? +
No. While it measures how variables influence mood, you must specify a relevant variable (like 'sleep hours' or 'stress score') when running evaluate_activity_impact.
What happens if I run identify_mood_anomalies too often? +
Running this tool frequently is fine, but remember it flags deviations from your norm. If you change your lifestyle drastically, the initial anomalies will just reflect that period of transition.
Do I need to manually clean my data before using Mood Pattern Detector? +
The MCP is designed to ingest existing logs. While cleaner data always helps, it manages basic formatting issues internally when processing the logs.
What kind of data format does analyze_weekly_cycle require? +
It requires structured mood logs that include timestamps and corresponding mood scores. The tool accepts standard CSV uploads, so just make sure your dates are consistent.
If I get an error using evaluate_activity_impact, what should I check first? +
First, verify the variable name you passed into the function. The system is case-sensitive and needs a clear match to your recorded activity type.
Can identify_mood_anomalies run on data from different sources? +
The tool processes all mood logs attached to your Vinkius account, regardless of the original entry source. It consolidates everything before running the analysis.
How do I optimize my setup for Mood Pattern Detector performance? +
For best results, ensure your historical data set covers at least three full months. More data points allow the tool to build a more accurate baseline model.
How does the detector identify weekly patterns? +
The analyze_weekly_pattern tool calculates the average mood score for each day of the week present in your logs and identifies significant differences between days.
Can I analyze specific activities like exercise or sleep? +
Yes, using evaluate_activity_impact, you can provide the name of any recorded variable to see its correlation with your mood levels.
What constitutes a mood anomaly? +
An anomaly is flagged by identify_mood_anomalies when a specific day's mood score deviates from your historical baseline beyond a defined sensitivity threshold.
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