MCP For Claude Marketplace
Feature Adoption Analytics

Feature Adoption Analytics MCP Connector for Claude

A+

Quantify feature success through adoption velocity, saturation estimates, and retention impact modeling.

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

This MCP server provides product managers with a specialized analytics engine to measure how effectively users integrate new features. By using tools like calculate_adoption_metrics, you can track adoption rates and predict when a feature will reach saturation. You can also use evaluate_retention_impact to determine if a feature is a driver of user loyalty, or simulate_growth_scenarios to model how improvements in onboarding or discoverability will affect future growth. It bridges the gap between raw usage data and actionable product insights.

adoptionretentionmetricsproduct-growthuser-behavior

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

predict_usage_segmentation

Categorizes the current user base into usage tiers to identify power users versus casual users

calculate_adoption_metrics

Provides a snapshot of current adoption progress and predicts the trajectory of the feature

simulate_growth_scenarios

Allows product managers to model how changing product variables affects future adoption

evaluate_retention_impact

Determines if a specific feature is a driver of user loyalty

See how to talk to your AI agent using Feature Adoption Analytics.

What is the current adoption progress for my new feature with 1000 total users, 200 feature users, and 10 days since launch?

The current adoption rate is 20.0%, with an adoption velocity of 2.0% per day and an estimated saturation in 45 days.

Is the new dashboard feature driving user loyalty? We have 500 feature users with 60% retention and 500 non-feature users with 45% retention.

The feature provides a retention lift of 0.15, which is categorized as a Critical impact on user loyalty.

How many power users do we have if the usage frequency is: user_1: 50, user_2: 5, user_3: 1, user_4: 12?

Based on the usage frequency, you have 1 power user, 1 regular user, 1 casual user, and 1 one-time user.

You can use the `calculate_adoption_metrics` tool, which provides a `saturationEstimate` representing the predicted number of days until the adoption rate levels off.

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