MCP For Claude Marketplace
AI Feature Discovery Analytics

AI Feature Discovery Analytics MCP Connector for Claude

A+

Measure and optimize how users find and engage with your AI features.

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

This MCP server provides a specialized analytics engine for SaaS products to measure the effectiveness of AI feature launches. It calculates core metrics like discovery rate, evaluates the performance of different discovery channels, and analyzes the velocity of user adoption. By using tools like calculate_discovery_metrics and analyze_discovery_velocity, product teams can identify friction in UI placement and generate actionable acceleration strategies to improve feature awareness and engagement.

ai-analyticsfeature-adoptionuser-engagementsaas-metricsdiscovery-rate

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

calculate_discovery_metrics

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

analyze_discovery_velocity

Measures the speed of adoption and identifies delays in feature awareness

evaluate_channel_performance

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

generate_acceleration_strategy

Provides actionable advice to improve discovery based on current performance gaps

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

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

The discovery rate is 25%.

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

The average days to discovery is 3.8 days.

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

Prioritize improving UI placement to reduce friction before increasing promotional efforts.

The discovery rate is the percentage of users who successfully identified and attempted to use an AI feature out of the total group exposed to it, calculated via `calculate_discovery_metrics`.

Related Connectors

Feature Adoption Analytics

Feature Adoption Analytics MCP

4 tools Official

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

A+ View details →
AI App Architecture Complexity Scorer

AI App Architecture Complexity Scorer MCP

4 tools Official

Quantify the structural intricacy and risks of AI-driven application architectures.

A+ View details →
AI Feature ROI Analyzer

AI Feature ROI Analyzer MCP

4 tools Official

Evaluate the financial efficiency and pricing viability of AI features at the user level.

A+ View details →
AI Feature Support Impact Analyzer

AI Feature Support Impact Analyzer MCP

4 tools Official

Calculate support burden, costs, and documentation ROI for AI features.

A+ View details →