MCP For Claude Marketplace
Grape Sampling Protocol Design

Grape Sampling Protocol Design MCP Connector for Claude

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Designs statistically valid grape sampling protocols using stratified random sampling.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner
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This MCP server provides advanced statistical tools for agricultural quality control. It allows AI agents to design precise sampling strategies for grape harvests. Use generate_sampling_plan to calculate the required number of samples based on lot size and heterogeneity. Use get_collection_pattern to determine the physical distribution of collection points. The server also includes analyze_variability_impact to evaluate vineyard vs. load variance and optimize_sampling_effort to balance labor costs with statistical precision.

grapesamplingstatisticsagricultureharvest

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

analyze_variability_impact

Evaluates how much of the total variance is driven by vineyard differences versus individual load differences

generate_sampling_plan

Calculates the core statistical parameters required to execute a sampling event

get_collection_pattern

Defines the spatial distribution of where samples should be physically collected

optimize_sampling_effort

Recommends a balance between cost (number of samples) and precision for decision-makers

See how to talk to your AI agent using Grape Sampling Protocol Design.

I have a 50-ton grape lot with a heterogeneity estimate of 1.5. I need a 95% confidence level and a precision of 0.5. How many samples do I need?

For a 50-ton lot with 1.5 heterogeneity, a 95% confidence level, and 0.5 precision, you need 36 total samples distributed across 4 recommended strata.

What is the best way to collect samples in a rectangular vineyard layout based on my sampling plan?

For a rectangular layout, the recommended approach is a grid-based stratified pattern to ensure even coverage across the vineyard blocks.

My maximum labor budget allows for 15 samples. How much precision can I expect for a 100-ton lot with 2.0 heterogeneity?

With a limit of 15 samples for a 100-ton lot and 2.0 heterogeneity, your predicted precision is +/- 1.2 Brix.

You can use the `generate_sampling_plan` tool. Provide the total lot size in tons, the estimated heterogeneity, your desired confidence level, and the target precision to receive a complete statistical plan.

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