# Measure AI Feature Discovery and Adoption Rates AI Agent Connect

> AI Feature Discovery Analytics measures how users find and engage with your AI features in a SaaS product. Product teams use this MCP to calculate core metrics like discovery rate and analyze adoption velocity. It evaluates which UI placements or marketing paths drive the most engagement, giving you actionable strategies to improve feature awareness and usage. Connect your preferred AI client to access this specialized analytics engine instantly.

## Overview
- **Category:** product-management
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_M2ugyR0bdwPEifqVrgkiN2rOjMAmxmSaK4RbYzhA/ai-agent-connect
- **Tags:** ai-analytics, feature-adoption, user-engagement, saas-metrics, discovery-rate

## Description

This MCP provides product teams a specialized analytics engine for measuring AI feature effectiveness. You can calculate core metrics, evaluate which discovery channels work best, and analyze the speed of user adoption. Instead of guessing where users get stuck, you get hard data. For instance, you can use the `calculate_discovery_metrics` tool to get a fundamental health score for any AI feature launch. If that score is low, you can use `evaluate_channel_performance` to determine if the problem is in your marketing copy or your UI placement. Finally, the MCP helps you identify the specific gaps and generate an acceleration strategy, giving you a clear plan to boost feature awareness and engagement.

## Tools

### calculate_discovery_metrics
Provides the fundamental health score of an AI feature's launch

### evaluate_channel_performance
Determines which marketing or UI paths are most successful at driving engagement

### analyze_discovery_velocity
Measures the speed of adoption and identifies delays in feature awareness

### generate_acceleration_strategy
Provides actionable advice to improve discovery based on current performance gaps

## Prompt Examples

**Prompt:** 
```
What is our current discovery rate if 1000 users saw the feature and 250 tried it?
```

**Response:** 
```
The discovery rate is 25%.
```

**Prompt:** 
```
How fast are users finding the feature? The discovery days are [2, 5, 3, 8, 1] and the UI placement is primary.
```

**Response:** 
```
The average days to discovery is 3.8 days.
```

**Prompt:** 
```
Give me a strategy for a feature with a 10% discovery rate, high UI friction, and low promotion intensity.
```

**Response:** 
```
Prioritize improving UI placement to reduce friction before increasing promotional efforts.
```

## Capabilities

### Calculate Feature Health Score
The AI uses this capability when you need a single, fundamental metric to gauge the overall success of a new AI feature launch.

### Compare Discovery Channels
It determines the most effective marketing or UI paths by comparing performance across different user entry points.

### Measure Adoption Speed
The MCP tracks and reports the average time it takes for users to find and start using the feature.

### Generate Improvement Plan
The AI creates a prioritized, actionable strategy when it detects performance gaps in your user adoption data.

## Use Cases

### Post-Launch Performance Review
You just launched a major AI widget. Use this MCP to calculate the discovery metrics and see if the rollout was successful.

### Onboarding Flow Optimization
The new feature isn't being used. Use the MCP to evaluate your onboarding paths and find the best way to introduce the tool.

### Marketing Campaign Audit
A specific marketing campaign ran for a month. Use the MCP to compare its performance against your standard channels to prove ROI.

### Identifying User Drop-off
You suspect users are getting lost between the main dashboard and the new feature. Use the MCP to analyze the discovery velocity and pinpoint the delay.

## Benefits

- You calculate the exact discovery rate, giving you a clear metric of feature visibility.
- The MCP identifies specific friction points in your UI, telling you exactly where users are dropping off.
- You get a measurable timeline of adoption, allowing you to predict when your feature will reach critical mass.
- It provides a prioritized action plan, so you know exactly what to fix next.

## How It Works

Connecting this MCP is simple. Your AI client connects to the Vinkius catalog, and you simply prompt the MCP with your raw usage data. The MCP then runs the necessary calculations and returns a clear, actionable analysis.

1. Connect your preferred AI client to the Vinkius catalog.
2. Prompt the MCP with the data you want analyzed (e.g., user IDs, interaction timestamps).
3. The MCP runs the relevant tool, such as `calculate_discovery_metrics`.
4. You receive a clear, plain-language report detailing the feature's health score and next steps.

## Frequently Asked Questions

**Does this MCP work for any type of feature, or just AI features?**
This MCP is specialized for measuring AI features within a SaaS product. It focuses on the unique challenges of user discovery and adoption for advanced, AI-driven tools.

**What kind of data does the MCP need to run?**
The MCP needs usage data, including metrics like user counts, feature interaction counts, and timestamps. The prompt examples show how to provide this raw data for calculation.

**Can I use this to compare different product versions?**
Yes. You can input data from different time periods or product versions to compare performance, helping you see if a change improved the discovery rate.

**Is this MCP just a report generator, or does it give advice?**
It does both. After calculating metrics, the MCP can use the `generate_acceleration_strategy` tool to provide concrete, actionable advice on how to improve adoption.