MCP For Claude Marketplace
AI Feature Onboarding Analyzer

AI Feature Onboarding Analyzer MCP Connector for Claude

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Analyze AI feature adoption efficiency using funnel metrics and TTV analysis.

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

This MCP server provides tools to measure the health of AI feature onboarding. It calculates completion rates, identifies user drop-off bottlenecks, and recommends optimization priorities based on feature complexity. Use analyze_funnel_metrics_tool to get core health indicators, identify_dropoff_bottlenecks_tool to find friction points, calculate_optimization_priority_tool for actionable product recommendations, and evaluate_ttv_efficiency_tool to assess time-to-value risks.

saasonboardingfunnelmetricsai-adoption

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

calculate_optimization_priority_tool

Recommend specific areas for product intervention based on complexity and friction

evaluate_ttv_efficiency_tool

Assess whether the time taken to reach value is acceptable given complexity

identify_dropoff_bottlenecks_tool

Pinpoint exactly where users are leaving the onboarding process

analyze_funnel_metrics_tool

Calculate core health indicators of the AI onboarding process

See how to talk to your AI agent using AI Feature Onboarding Analyzer.

Calculate the funnel metrics for 1000 users who started, 450 who completed, over 5 steps.

The completion rate is 45%, the average time to value is 3.2 days, and the funnel health score is 72.

Where are users dropping off? Here is the data: stepName: 'Account Setup', usersLost: 50; stepName: 'AI Configuration', usersLost: 120.

The critical step is 'AI Configuration' with a high severity rating due to the significant user loss.

What should I do if my feature has a complexity of 8 and a completion rate of 20%?

The priority level is High. It is recommended to simplify feature entry to improve the completion rate.

You can track completion rates, average time to value, funnel health scores, and specific drop-off bottlenecks using `analyze_funnel_metrics_tool` and `identify_dropoff_bottlenecks_tool`.

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