MCP For Claude Marketplace
Startup Onboarding Effectiveness

Startup Onboarding Effectiveness MCP Connector for Claude

A+

Quantify onboarding success by analyzing speed to value, completion rates, and retention.

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

This MCP server provides a measurement engine to quantify user onboarding success. It analyzes Time to First Value (TTFV), completion rates, and long-term retention across different user segments. Use get_onboarding_score to calculate a health indicator, analyze_funnel_dropoffs to find friction points, calculate_retention_correlation to link onboarding to retention, and compare_segment_performance to evaluate different user tiers.

onboardingretentionfunnel-analysismetricsproduct-analytics

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

calculate_retention_correlation

Determines if successful onboarding is actually driving long-term user retention

compare_segment_performance

Compares onboarding effectiveness across different user tiers or categories

analyze_funnel_dropoffs

Identifies which specific steps in the onboarding process cause the most user attrition

get_onboarding_score

Calculates a single health score representing how effectively users are being onboarded

See how to talk to your AI agent using Startup Onboarding Effectiveness.

What is our current onboarding health score if TTFV is 2 hours, completion rate is 75%, and we had 5 support tickets?

Your onboarding health score is 85, which is rated as Excellent.

Where is the main bottleneck in our onboarding funnel if the user counts per step are [1000, 800, 200, 150]?

The main bottleneck is at step index 1, where there was a 75% drop-off.

Is there a correlation between our completion rates [0.5, 0.6, 0.7] and 30-day retention [0.2, 0.3, 0.4]?

Yes, there is a strong correlation coefficient of 1.0, indicating a significant relationship between onboarding completion and retention.

The score is a composite metric derived from the inverse of Time to First Value and support ticket volume, combined with the completion rate.

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