MCP For Claude Marketplace
Wine Quality Score Analysis

Wine Quality Score Analysis MCP Connector for Claude

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Statistical analysis of sensory evaluation scores for wine quality.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner
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This MCP server provides statistical tools to analyze sensory evaluation data from wine tasting panels. It helps researchers and quality control teams understand wine quality through mean scores, assess panel reliability via agreement metrics, and verify the panel's ability to distinguish between samples. Use get_wine_summary to see average scores, evaluate_panel_consistency to check panelist agreement, detect_discriminant_capability to verify sample distinction, and identify_panelist_outliers to find biased scorers.

winesensory-evaluationanovaquality-controlstatistics

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

identify_panelist_outliers

Finds panelists whose scoring behavior deviates significantly from the group norm

detect_discriminant_capability

Checks if the panel is actually capable of telling the wines apart

evaluate_panel_consistency

Determines how well the panelists agree with one another

get_wine_summary

Provides a high-level overview of the mean quality scores for all wines in the dataset

See how to talk to your AI agent using Wine Quality Score Analysis.

Show me the average quality scores for all the wines in this dataset.

The average scores are: Cabernet Sauvignon: 8.2, Merlot: 7.5, and Pinot Noir: 7.9.

Is the tasting panel capable of distinguishing between these different wines?

Yes, the panel has high discriminant power, showing significant differences between the wine varieties.

Are there any panelists providing biased scores?

Panelist ID P-402 has been identified as an outlier due to a high deviation score.

It processes sensory evaluation scores, including wine IDs, panelist IDs, and numerical scores assigned during tastings.

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