MCP For Claude Marketplace
AI Engagement Scoring

AI Engagement Scoring MCP Connector for Claude

A+

Analyze AI feature engagement, adoption rates, and churn risk.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner
More Details Connect to Claude

This MCP server provides deep insights into how users interact with AI capabilities. It allows agents to calculate a holistic engagement score, track feature adoption through discovery and realization rates, monitor engagement trends, and predict churn risk. By using tools like calculate_user_engagement_score and predict_user_churn_risk, agents can distinguish between simple feature testing and true value realization in AI workflows.

engagementchurnadoptionanalyticsai-metrics

4 tools expose this connector's capabilities to your AI agent.

calculate_user_engagement_score

Determine the current engagement level of a specific user

get_feature_adoption_metrics

Evaluate how well specific AI features are being discovered and adopted

predict_user_churn_risk

Identify users at risk of abandoning AI features

analyze_engagement_trend

Track the trajectory of a user's interest in AI features over time

See how to talk to your AI agent using AI Engagement Scoring.

What is the current engagement score for user_123?

The current engagement score for user_123 is 85, indicating a Power User with high value realization.

Is user_456 at risk of churning from our AI features?

User_456 is flagged with a high risk level due to a declining engagement trend and low recent value realization.

How is the 'smart_summary' feature performing in terms of adoption?

The 'smart_summary' feature has a high discovery rate of 75% but a lower realization rate of 30%, suggesting users are trying it but not finding consistent value.

The score is determined by `calculate_user_engagement_score`, which evaluates session volume, feature breadth, and the ratio of successful value realizations to total AI outputs.

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