MCP For Claude Marketplace
Wine Score Prediction Model

Wine Score Prediction Model MCP Connector for Claude

A+

Predict professional critic scores and identify score drivers for wines.

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

This MCP server provides a predictive engine to estimate professional critic scores for wines. By analyzing varietal data, vintage conditions, winemaking methods, and chemical profiles, it calculates predicted score ranges and confidence intervals. Use predict_wine_score to get a score estimate, analyze_score_drivers to understand what influences the rating, evaluate_improvement_opportunities to suggest technical changes for higher scores, and get_critic_profile to account for specific critic preferences.

winepredictioncriticviticultureanalytics

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

analyze_score_drivers

Identify attributes responsible for the predicted score

evaluate_improvement_opportunities

Suggest changes to increase the predicted score

get_critic_profile

Retrieve historical scoring tendencies of a specific critic

predict_wine_score

Predict a professional critic score for a wine based on its profile

See how to talk to your AI agent using Wine Score Prediction Model.

Predict the score for a 2018 Cabernet Sauvignon from Napa Valley with oak aging and a pH of 3.5.

The predicted score for this Cabernet Sauvignon is 92, with a confidence interval of 90-94.

What are the main drivers for this wine's score?

The primary positive driver is the high alcohol content, while the moderate acidity is a slight negative driver for this specific profile.

How can I improve the score of my current wine profile to reach a 95?

To reach a 95, consider increasing the barrel aging duration and slightly reducing the residual sugar levels.

Scores include a confidence interval based on historical data density for similar wine profiles, providing a range rather than a single fixed number.

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