MCP For Claude Marketplace
Product-Market Fit Diagnostic Engine

Product-Market Fit Diagnostic Engine MCP Connector for Claude

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Evaluates product maturity and market resonance using retention, sentiment, and growth signals.

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

This MCP server provides a diagnostic engine to evaluate how well a product satisfies market needs. It synthesizes key metrics like retention curves, the Sean Ellis test, and NPS into a unified readiness score. Use calculate_pmf_score to get a quantitative health assessment, analyze_retention_health to check if your user base is stabilizing, evaluate_growth_efficiency to measure market pull, and get_improvement_priorities to identify critical business interventions.

pmfretentiongrowthnpsproduct-metrics

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

analyze_retention_health

Evaluates whether the user base is stabilizing or leaking

calculate_pmf_score

Provides the primary quantitative assessment of product health

get_improvement_priorities

Identifies specific areas of the business model that require intervention to increase PMF

evaluate_growth_efficiency

Determines if growth is sustainable and driven by market demand

See how to talk to your AI agent using Product-Market Fit Diagnostic Engine.

Calculate my PMF score with 30% retention, 40 NPS, 10% organic growth, 5% referral rate, and 35% Sean Ellis score.

Your PMF score is 42, indicating a Foundational readiness level with a moderate confidence rating.

Analyze this retention data: 100, 50, 30, 25, 22, 21.

The retention curve is flattened, indicating a stable user base with a Strong health status.

Is my growth efficient with 15% organic growth, 10% referral rate, and $5.00 paid acquisition cost?

Your growth efficiency is High, with a viral coefficient of 0.25 and a sustainability score of 75.

The score is a weighted composite of user retention, NPS, organic growth, referral rates, and the Sean Ellis 'very disappointed' percentage via the `calculate_pmf_score` tool.

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