Use Wine Tasting Panel Design with your AI.
Connect your account once and let the AI you already use work with it, without building another integration. Design sensory tasting panels for wine quality control by calculating panelist requirements and reliability.
Developed, maintained, and hosted by Vinkius.
MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED
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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.
Complete set · 4 capabilities
The complete Wine Tasting Panel Design capability set.
These are the exact actions your AI can choose when you ask it to work with Wine Tasting Panel Design.
01-04
4 capabilities in this set.
Part of 4 available through Wine Tasting Panel Design.
- 01
Calculate panel requirements
Determines the minimum number of panelists and replications needed for a specific sensory test
- 02
Evaluate panel performance
Analyzes historical or current tasting data to assess the reliability of the panel
- 03
Predict test reliability
Estimates the likelihood that the designed panel will successfully meet its objectives before the test begins
- 04
Select qualified panelists
Filters a list of available panelists to find those who meet the specific requirements of a designed test
One connector, every AI
Wine Tasting Panel Design works with the most popular AI clients.
These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.
Claude
ChatGPT
Gemini
Perplexity
Grok
Microsoft Copilot
Cursor
VS Code
Windsurf
JetBrains
Cline
LangChain
Vercel AI SDK
Lovable
Z.ai
Raycast
Qwen Code
Kimi Code
Le ChatBuilding your own app? The connector is yours to use.
You don't need a client to put Wine Tasting Panel Design to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.
Connect your client
One URL. Every client.
Activate the Connector, copy your link, and paste it into the client you already use. 4 capabilities arrive ready to run.
Preview access · not provider authentication
The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Wine Tasting Panel Design, so you can see the experience inside your AI.
It does not authenticate your account with Wine Tasting Panel Design. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.
Wine Tasting Panel Design Connector
You're all set. Choose your MCP client and follow the setup instructions.
https://edge.vinkius.com/vk_preview_9M3OfxVVHqQctzp4DS59v1jDYSK6pOiwGpZNQIQN/mcpClaude Desktop
Follow the steps below to connect in seconds.
- 1In Claude Desktop, open Settings → Connectors.
- 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
- 3Click Add and start a new chat — Wine Tasting Panel Design capabilities are ready to use.
{
"mcpServers": {
"wine-tasting-panel-design-mcp": {
"url": "https://edge.vinkius.com/vk_preview_9M3OfxVVHqQctzp4DS59v1jDYSK6pOiwGpZNQIQN/mcp"
}
}
}
Claude
ChatGPT
Cursor
VS Code
Windsurf
Claude Code
JetBrains
Cline
Step-by-step instructions for each client are in the guide. How to connect
Guided setup for Claude? How to give Claude access to Wine Tasting Panel Design
FAQ
Questions Wine Tasting Panel Design owners ask.
- 01
How do I determine how many people I need for a descriptive test?
You can use the calculate_panel_requirements capability by providing the test type as 'descriptive', your desired statistical power, and the expected sensitivity and consistency of your panel.
- 02
Can I filter panelists by their specific scores?
Yes, the select_qualified_panelists capability allows you to filter available candidates using minimum sensitivity and consistency thresholds.
- 03
How can I check if my panel is reliable enough for a new test?
Use the predict_test_reliability capability. It takes your design parameters and current panel performance to provide a reliability score and risk level.
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