# AI Feature Usage Analytics for Product Teams. AI Agent Connect

> AI Feature Usage Analytics gives your agent deep insights into how users interact with AI capabilities. This MCP helps you move beyond simple usage counts, tracking everything from stickiness ratios to usage intensity distribution. You can compare behavior across different user tiers and map out engagement trends to understand true product health. It’s built for product managers who need to know if their AI features are actually sticking with users.

## Overview
- **Category:** analytics
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_NXLBxo4KbFu77cH9p5U7rwUVSPswlxRdaH1pfsRz/ai-agent-connect
- **Tags:** engagement, stickiness, usage-patterns, user-segments, ai-metrics

## Description

You need to know if your AI features are just novelty buttons or core parts of the user experience. This MCP provides the data to answer that question. It lets your agent calculate key metrics like stickiness (DAU/MAU) and analyze how usage intensity is spread across your user base. You can track engagement trends over time, seeing if users are getting more or less invested. Plus, you can compare performance between different user segments, figuring out which tiers are adopting the AI features the most. This MCP gives you the raw data to prove product value.

## Tools

### get_engagement_trajectory
Determines the direction and velocity of AI engagement

### get_segment_comparison
Compares AI engagement metrics across different user tiers

### get_stickiness_metrics
Calculates the stickiness ratio (DAU/MAU) for a specific user segment

### get_usage_distribution
Analyzes the distribution of AI usage intensities across users

## Prompt Examples

**Prompt:** 
```
What is the stickiness ratio for the pro segment with 500 daily users and 2000 monthly users?
```

**Response:** 
```
The stickiness ratio for the pro segment is 0.25.
```

**Prompt:** 
```
Is engagement improving for the enterprise segment if sessions went from 100 to 150?
```

**Response:** 
```
Yes, the engagement trajectory is improving with a growth rate of 50.0%.
```

**Prompt:** 
```
Show me the usage distribution for text-generation with sessions [1, 5, 10, 20, 50].
```

**Response:** 
```
The distribution for text-generation is: low-frequency: 3 users, medium-frequency: 1 user, high-frequency: 1 user.
```

## Capabilities

### Calculate Stickiness Ratio
The agent uses this when you need to know the ratio of daily to monthly active users for a specific segment.

### Analyze Usage Spread
The agent uses this to map out how frequently users are engaging with the AI features.

### Track Engagement Trends
The agent uses this to determine if user interest in the AI features is increasing or decreasing over time.

### Compare User Tiers
The agent uses this when you want to compare the performance of AI features across different customer segments.

## Use Cases

### Evaluating Feature Launch Success
After launching a new AI tool, you ask the agent to get_engagement_trajectory to see if the usage is trending up or down month over month.

### Tiered Pricing Justification
You use get_segment_comparison to show stakeholders that the 'Enterprise' tier has significantly higher AI feature adoption than the 'Basic' tier.

### Identifying Power Users
You run get_usage_distribution to see if your feature usage is concentrated among a small group of users, signaling a potential bottleneck.

### Assessing Product Health
You calculate get_stickiness_metrics to determine if the overall user base is relying on the AI features daily or only occasionally.

## Benefits

- You measure true product stickiness using the DAU/MAU ratio for specific user groups.
- You identify if AI features are driving growth or if engagement is slowing down over time.
- You compare performance across user tiers to pinpoint which segments need better feature adoption.
- You understand the depth of usage by analyzing how many users are light users versus power users.

## How It Works

Connecting this MCP is simple. You connect your preferred AI client once through Vinkius, and the tool becomes available to your agent. You then prompt your agent with a specific question, and it runs the necessary analytics to return the data.

1. Connect your AI client to the Vinkius catalog.
2. Prompt your agent with a specific analytics question (e.g., 'What is the stickiness ratio for X?').
3. The agent invokes the appropriate tool (e.g., get_stickiness_metrics).
4. The MCP runs the calculation and returns the specific, actionable metric to your chat window.

## Frequently Asked Questions

**Does this MCP track all AI feature usage?**
The MCP provides deep insights into how users interact with AI capabilities. It is designed to calculate metrics like stickiness and usage distribution for the features you track.

**What is the difference between stickiness and engagement?**
Stickiness measures the ratio of daily to monthly active users for a segment. Engagement trajectory, however, tracks the overall direction and velocity of user interest over time.

**Can I compare different user groups?**
Yes. You can use the get_segment_comparison tool to compare AI engagement metrics across different user tiers, helping you spot performance gaps.

**Is this only for large companies?**
No. It helps any product team analyze their AI features. You can analyze usage intensity and engagement trends regardless of your company size.