MCP For Claude Marketplace
Olive Oil Extraction Yield & Quality Predictor

Olive Oil Extraction Yield & Quality Predictor MCP Connector for Claude

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Predict olive oil yield, extraction loss, and chemical quality risks based on fruit characteristics and malaxing conditions.

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

This MCP server provides advanced modeling for olive oil production. It allows AI agents to calculate expected oil yield and extraction loss using predict_yield_and_loss. It also estimates chemical quality risks, such as Free Fatty Acids and peroxide levels, via predict_quality_risk. For producers looking to balance output and quality, optimize_malaxing_params suggests the ideal temperature and duration for the malaxing stage based on the specific olive variety and extraction method.

olive-oilextractionyield-predictionquality-controlagriculture-tech

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

optimize_malaxing_params

predict_quality_risk

predict_yield_and_loss

See how to talk to your AI agent using Olive Oil Extraction Yield & Quality Predictor.

Calculate the expected yield for 1 tonne of Arbequina olives with 45% moisture and 20% dry oil content using a two-phase decanter at 25C for 20 minutes.

The expected yield is 185.5 kg of oil per tonne, with an extraction loss of 12.2 kg.

What is the quality risk for Koroneiki olives if I malax at 35C for 45 minutes?

The predicted FFA is 0.45% and the peroxide value risk is MEDIUM.

Suggest optimal malaxing settings for Picual olives to get the best quality possible using a three-phase method.

The optimal settings are a temperature of 22.5C and a duration of 15 minutes.

The predictions are based on mathematical models of fruit variety, moisture, and extraction method. Using `predict_yield_and_loss` provides a calculated estimate of kilograms of oil per tonne of fruit.

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