MCP For Claude Marketplace
Product-Market Fit Score Engine

Product-Market Fit Score Engine MCP Connector for Claude

A+

Quantify product-market fit using Sean Ellis methodology and business metrics.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner
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This MCP server provides a diagnostic engine to quantify Product-Market Fit (PMF). It synthesizes qualitative user sentiment from the Sean Ellis test with quantitative business health metrics like retention and organic growth. Use calculate_sentiment_metrics to analyze survey responses, calculate_pmf_composite_score to generate a 0-100 index, analyze_cohort_variance to adjust for early adopter bias, and get_market_readiness_guidance to receive actionable business directions.

pmfproduct-metricssean-ellisgrowthretention

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

analyze_cohort_variance

Adjusts the PMF score to account for potential bias between early adopters and the broader market

calculate_pmf_composite_score

Generates the final 0-100 PMF score by combining qualitative sentiment and quantitative business metrics

calculate_sentiment_metrics

Analyzes raw survey responses to determine the qualitative health of the product

get_market_readiness_guidance

Translates the PMF score and readiness level into actionable business directions

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

Analyze these survey responses: ['very disappointed', 'somewhat disappointed', 'very disappointed', 'not disappointed'].

The very disappointed percentage is 50% with a total of 4 responses.

Calculate the PMF score with 40% very disappointed, 30 NPS, 45% retention, and 15% organic growth.

The calculated PMF score is 65, indicating an Optimization readiness level.

What is the recommended action for a PMF score of 85 and 20% retention?

The primary focus should be on Product Stability due to the high score paired with low retention.

It is a qualitative survey method that asks users how they would feel if they could no longer use the product, identifying the 'very disappointed' segment.

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