# Distraction Time Total is a focus MCP. AI Agent Connect

> Distraction Time Total helps you measure exactly how much time slips away during your workday. Your AI client uses this MCP to aggregate distraction events, calculate average interruption lengths, and track how often you lose focus. It turns raw interruption logs into actionable metrics so you can see where your attention actually goes.

## Overview
- **Category:** productivity
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_qGnH69UpoOteMVxxUBpkN30XzKIBNh1fheCz2znB/ai-agent-connect
- **Tags:** productivity, time-tracking, focus, analytics, distraction

## Description

You can't fix what you don't measure. This MCP gives your AI client the ability to dig into your distraction logs and pull out the numbers that matter. Instead of guessing how much time you lost to meetings or notifications, you can get hard data on your focus patterns. 

Your agent can sum up every single interruption to find your total lost time or look at specific time buckets to see when you're most vulnerable to losing focus. It also handles the math for you, calculating the average length of an interruption so you know if you're dealing with quick pings or deep-work killers. If your data looks messy, the MCP can check it for errors to ensure your analysis stays accurate. It's a direct way to turn messy interruption records into a clear picture of your daily productivity.

## Tools

### get_average_distraction_duration
This tool calculates the typical length of your interruption events. It helps your agent understand if your distractions are brief or long.

### get_distraction_frequency_metrics
This tool breaks down how many distractions occur within specific time windows. Use it to find patterns in when you lose focus.

### get_total_distraction_time
This tool sums up all recorded distraction events. It gives your agent the total amount of time lost to interruptions.

### validate_distraction_data
This tool checks your interruption logs for errors or inconsistencies. It ensures your data is clean before you run any analysis.

## Prompt Examples

**Prompt:** 
```
What is the total time lost if I had distractions of 5, 10, and 15 minutes?
```

**Response:** 
```
The total time lost is 30 minutes across 3 distraction events.
```

**Prompt:** 
```
What was the average distraction length for 2, 4, and 6 minutes?
```

**Response:** 
```
The average distraction duration is 4 minutes.
```

**Prompt:** 
```
Check if these distraction durations are valid: [10, 20, -5]
```

**Response:** 
```
The data is invalid because it contains a negative duration.
```

## Capabilities

### Total Time Calculation
Your agent sums up all recorded interruptions to find the total time lost.

### Frequency Analysis
The MCP groups interruptions into time buckets to show when you are most distracted.

### Average Duration Math
Your AI client finds the mean length of your distraction events.

### Data Integrity Checks
The MCP validates your logs to catch errors like negative time entries.

## Use Cases

### Deep Work Analysis
Your agent calculates how much time you actually spend in flow versus how much is lost to pings.

### Schedule Optimization
Use frequency metrics to find the best times of day to schedule meetings without breaking focus.

### Data Cleaning
Run validation on messy logs to ensure your productivity reports are accurate.

### Impact Assessment
Determine if frequent short distractions or rare long distractions are hurting your output more.

## Benefits

- Aggregates raw interruption logs into single, usable numbers.
- Identifies specific time periods where distractions are most frequent.
- Detects errors in your distraction records automatically.
- Provides the math needed to understand the impact of interruptions.

## How It Works

Connect the MCP to your client and start querying your distraction data.

1. Connect the MCP to your preferred client like Claude or Cursor.
2. Provide your distraction logs to your AI agent.
3. Ask the agent to run specific tools like total time or frequency metrics.
4. Review the calculated data and patterns provided by the agent.

## Frequently Asked Questions

**How do I use this MCP with Claude?**
You connect the MCP through the Vinkius platform. Once connected, you can ask Claude to use the tools directly in your chat.

**Can this MCP fix my broken distraction logs?**
It uses the validation tool to identify errors like negative durations, but you will need to correct the source data yourself.

**What kind of data does the MCP need?**
The MCP works with recorded distraction durations and timestamps to calculate totals, averages, and frequencies.

**Does this work with Cursor or Windsurf?**
Yes, this MCP is compatible with any MCP-compatible client including Cursor and Windsurf.

**Can I see when I am most distracted during the day?**
Yes, the frequency metrics tool allows your agent to group distractions into time buckets to show you these patterns.

**How do I calculate my total lost time?**
You can use the `get_total_distraction_time` tool by providing an array of all your recorded distraction durations.

**Can I see how frequent my distractions are?**
Yes, the `get_distraction_frequency_metrics` tool allows you to group distractions into specific time buckets to see patterns.

**How can I check if my distraction data is valid?**
The `validate_distraction_data` tool checks the integrity of your data to ensure all durations are positive and numeric.