Vinkius
Mood Pattern Detector

Mood Pattern Detector MCP for AI. Find what patterns move your emotional baseline.

Claude Claude
ChatGPT ChatGPT
Cursor Cursor
Gemini Gemini
Windsurf Windsurf
VS Code VS Code
JetBrains JetBrains
Vercel Vercel
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Mood Pattern Detector MCP on Cursor AI Code EditorMood Pattern Detector MCP on Claude Desktop AppMood Pattern Detector MCP on OpenAI Agents SDKMood Pattern Detector MCP on Visual Studio CodeMood Pattern Detector MCP on GitHub Copilot AI AgentMood Pattern Detector MCP on Google Gemini AIMood Pattern Detector MCP on Lovable AI DevelopmentMood Pattern Detector MCP on Mistral AI AgentsMood Pattern Detector MCP on Amazon AWS Bedrock

Connect to your AI in seconds.

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.

Pinpoint weekly emotional trends

Determine which days or times of the week consistently correlate with specific emotional states.

Measure variable influence on mood

Quantify how changes in recorded activities, like sleep duration or workout intensity, affect your average mood score.

Flag sudden mood shifts

Automatically identify specific dates where your emotional state was significantly different from your historical norm.

Analyze cyclical patterns

Uncover repeating behavioral or emotional cycles over time, such as a recurring 'Sunday Scaries' pattern.

Included with Plan

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AI Agent

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.

Make your AI actually useful.

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 Vinkius

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...

Identify Mood Anomalies

Flags dates where your reported mood was significantly different from the average...

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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

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 Mood Pattern Detector 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 Mood Pattern Detector, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.

  • Use this MCP plus 5,100+ others, all in one place
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  • Works with Claude, ChatGPT, Cursor, and more
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Mood Pattern Detector 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 Mood Pattern Detector. 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.

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.

Built · Hosted · Managed by Vinkius Mood Pattern Detector MCP - Track mood cycles and activity impacts
Server ID 019ed645-49cb-71d3-b884-eb582a5dce42
Vinkius Inspector
Compliance Grade A+
Score 100/100
Vinkius Inspector Badge — Score 100/100

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.

Built & Managed by Vinkius 30s setup 3 tools

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All 3 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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