# Measure AI's true impact on your NPS. AI Agent Connect

> AI Feature NPS Impact Engine quantifies how your product's Net Promoter Score (NPS) changes when users interact with AI features. This MCP helps product teams move past gut feelings. You can attribute specific sentiment shifts to AI usage, pinpoint high-risk user groups, and translate raw satisfaction data into a clear business value metric. It tells you if your AI is a core driver of loyalty or just a source of confusion.

## Overview
- **Category:** product-management
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_jHkDc4P6O0vpL4pfOavrHt2AJqoEz61FC4hG6fkc/ai-agent-connect
- **Tags:** nps, ai-impact, sentiment-analysis, user-retention, product-analytics

## Description

This MCP provides specialized analytics to measure the specific impact of AI features on your Net Promoter Score. Instead of guessing, you can attribute sentiment shifts directly to AI usage. Product teams use this tool to understand if adding AI is genuinely making users more loyal, or if it's actually causing friction. You can identify which user segments are most affected by AI changes and translate complex satisfaction data into actionable business value. It helps you decide where to focus your development efforts.

## Tools

### aggregate_segment_impact
Provides a high-level summary of how AI is impacting different user demographics

### calculate_nps_lift
Determines how much the AI feature is driving or dragging the overall product NPS

### evaluate_detractor_risk
Predicts the likelihood of users becoming detractors based on their specific AI-related grievances

### quantify_feature_value
Translates NPS and satisfaction data into a business-centric Value Perception metric

## Prompt Examples

**Prompt:** 
```
Calculate the NPS lift for an Enterprise user in a Data Analysis workflow where NPS with AI is 50 and without AI is 30.
```

**Response:** 
```
The NPS lift is 20, and the AI feature is acting as a Driver for this segment.
```

**Prompt:** 
```
What is the detractor risk for a daily user with an AI satisfaction score of 2 and reasons including 'hallucinations'?
```

**Response:** 
```
The risk level is Critical, with 'hallucinations' identified as the primary risk driver.
```

**Prompt:** 
```
Determine the value perception for a feature with an NPS lift of 15 and AI satisfaction of 80 in a Content Creation use case.
```

**Response:** 
```
The feature is categorized as an Essential core value driver.
```

## Capabilities

### Segment Impact Analysis
Use this when you need a summary of how AI affects different user demographics.

### NPS Lift Calculation
Run this when you need to know the precise change in NPS caused by an AI feature.

### Detractor Risk Scoring
Check this when you want to predict which users are likely to become unhappy because of AI issues.

### Value Perception Metric
Generate this when you need to translate satisfaction scores into a business value metric.

### Sentiment Attribution
Determine the specific link between user sentiment and AI feature usage.

## Use Cases

### Post-Launch Feature Review
After rolling out a new AI feature, use this MCP to immediately calculate the NPS lift and see if the launch was successful.

### Identifying User Pain Points
Run the detractor risk tool to find out if users are complaining about specific AI behaviors, like hallucinations.

### Prioritizing Development
Aggregate segment impact data to decide which user group needs the most attention or which AI feature should be built next.

### Stakeholder Reporting
Use the feature value tool to present a clear, quantified business case for continued AI investment to executives.

## Benefits

- You calculate the exact NPS lift, proving whether AI usage is driving or dragging your overall product score.
- The MCP identifies specific user segments that are most sensitive to changes in AI features.
- You predict which users are at high risk of becoming detractors based on their AI-related complaints.
- It converts abstract satisfaction data into a quantifiable Value Perception metric for stakeholders.

## How It Works

Connect your preferred AI client to the Vinkius catalog. You call the specific tool, providing the necessary data points like NPS scores and user segments. The MCP processes the analytics and returns a clear, actionable metric.

1. Connect your AI client to the Vinkius catalog.
2. Select the specific tool, like `calculate_nps_lift`.
3. Input the required data, such as pre-AI and post-AI NPS scores.
4. Receive a direct result showing the NPS change and its business implication.

## Frequently Asked Questions

**Does this MCP tell me *why* users are unhappy with AI?**
It helps you pinpoint the risk. The `evaluate_detractor_risk` tool predicts the likelihood of becoming a detractor based on specific AI-related grievances, giving you the key areas to investigate.

**Can I use this to compare AI impact across different user types?**
Yes. You can use `aggregate_segment_impact` to get a high-level summary of how AI affects different user demographics, letting you compare groups easily.

**What kind of data does it need to calculate NPS lift?**
The `calculate_nps_lift` tool requires comparison data, specifically the NPS score before the AI feature was used and the score after its implementation.

**Is this just a dashboard, or does it give me actionable metrics?**
It's an analytics engine, not a dashboard. It gives you actionable metrics, like a quantified Value Perception score, which you can use directly in your product roadmap.