# Wine Score Prediction Model AI Agent Connect

> Predict professional critic scores and identify score drivers for wines.

## Overview
- **Category:** agriculture
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_X1N8Ct65WibzxX8d7ykwMURjO5eWH6mPfj5sInN0/ai-agent-connect
- **Tags:** wine, prediction, critic, viticulture, analytics

## Description

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.

## Tools

### 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

## Prompt Examples

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

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

**Prompt:** 
```
What are the main drivers for this wine's score?
```

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

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

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

## Frequently Asked Questions

**How accurate are the predicted scores?**
Scores include a confidence interval based on historical data density for similar wine profiles, providing a range rather than a single fixed number.

**Can I adjust predictions for specific critics?**
Yes, by using the `targetCritic` parameter in `predict_wine_score`, the model adjusts for the historical scoring tendencies and stylistic preferences of that specific critic.

**What data is required for a prediction?**
You need to provide the grape variety, region, vintage year, winemaking techniques, and analytical chemical parameters like pH and alcohol content.