MCP For Claude Marketplace
AI Feature NSM Analyzer

AI Feature NSM Analyzer MCP Connector for Claude

A+

Calculates AI feature impact on North Star Metrics using correlation and leading indicators.

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

This MCP server provides a mathematical framework to measure how AI-driven features drive a company's North Star Metric (NSM). By analyzing the relationship between feature usage and core product goals, it enables data-driven product decisions. Use get_nsm_contribution to find the percentage of metric movement attributed to a feature, evaluate_leading_indicator to assess predictive signals, and rank_feature_priority to balance mathematical impact with strategic alignment. It helps product teams move from intuition to precise impact measurement.

nsmai-impactmetricsproduct-strategydata-analysis

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

rank_feature_priority

Generates a final priority ranking for an AI feature by combining mathematical impact with strategic intent

evaluate_leading_indicator

Determines how predictive a specific leading indicator is for the North Star Metric

get_feature_impact_summary

Provides a high-level overview of a feature's health and contribution status

get_nsm_contribution

Calculates the specific percentage of the North Star Metric that is driven by a specific AI feature

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

What is the contribution of our new autocomplete feature to our Daily Active Users NSM?

The autocomplete feature contributes 4.5% to the Daily Active Users NSM, with a medium impact magnitude.

How reliable is the 'AI prompt completion' metric as a signal for our North Star Metric?

The 'AI prompt completion' metric has a predictive strength of 0.85 and is considered a Stable signal.

Should we prioritize the 'Smart Summary' feature over 'Voice Input'?

Based on the current metrics, 'Smart Summary' is ranked as P0 - Immediate due to its high NSM contribution and strategic alignment, while 'Voice Input' is ranked as P2 - Backlog.

The contribution is determined by the product of the feature usage volume and its correlation strength, normalized against the total NSM value using the `get_nsm_contribution` tool.

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