MCP For Claude Marketplace
Wine Label Design Testing

Wine Label Design Testing MCP Connector for Claude

A+

Design valid A/B testing protocols for wine labels using statistical experimental design.

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

This MCP server provides specialized tools for wine packaging experts to design and analyze A/B tests. Use calculate_experiment_requirements to determine the necessary sample size and test duration for statistical validity. You can use evaluate_label_performance to compare two design variants and determine if a winner is statistically significant. Additionally, simulate_shelf_impact allows you to adjust predicted scores based on lighting or retailer context, while segment_test_results helps break down consumer responses by demographic attributes like age or region.

wineab-testingpackagingconsumer-researchexperimental-design

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

calculate_experiment_requirements

Determines the foundational parameters needed to launch a valid A/B test

evaluate_label_performance

Compares the performance of two label variants against the target metric

segment_test_results

Breaks down the experimental results into specific consumer groups

simulate_shelf_impact

Adjusts predicted performance based on environmental and consumer context

See how to talk to your AI agent using Wine Label Design Testing.

I want to test a new label for a premium Chardonnay. What sample size do I need for a 95% confidence level and 80% power to detect a 5% difference in shelf appeal?

To detect a 5% difference in shelf appeal with 95% confidence and 80% power, you will need a required sample size of 1,536 participants, with an estimated test duration of 12 days.

Variant A scored 7.5 and Variant B scored 8.2. Variant A had 500 participants and Variant B had 500 participants. Is there a winner?

Yes, Variant B is the winner. The metric difference is 0.7, and the result is statistically significant.

A label has a base score of 75. How will it perform in a premium boutique setting?

In a premium boutique setting, the adjusted score for the label is 84.5 due to the positive context multiplier.

You can use the `calculate_experiment_requirements` tool. By providing your target metrics, desired power, and confidence level, it will return the exact required sample size needed for a valid test.

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