Use Wine Tasting Fee Optimizer with your AI.
Connect your account once and let the AI you already use work with it, without building another integration. Optimize tasting room profitability by balancing fees and wine sales.
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 Fee Optimizer capability set.
These are the exact actions your AI can choose when you ask it to work with Wine Tasting Fee Optimizer.
01-04
4 capabilities in this set.
Part of 4 available through Wine Tasting Fee Optimizer.
- 01
Calculate group pricing impact
Adjusts the individual optimization to account for group dynamics and higher operational overhead
- 02
Get optimal fee structure
Determines the single most profitable tasting fee and the corresponding purchase threshold for a waiver
- 03
Simulate weekday vs weekend sensitivity
Compares how different fee structures perform during low-demand weekdays versus high-demand weekends
- 04
Get revenue projections
Forecasts total revenue for a given period based on a specific fee configuration
One connector, every AI
Wine Tasting Fee Optimizer 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 Fee Optimizer 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 Fee Optimizer, so you can see the experience inside your AI.
It does not authenticate your account with Wine Tasting Fee Optimizer. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.
Wine Tasting Fee Optimizer Connector
You're all set. Choose your MCP client and follow the setup instructions.
https://edge.vinkius.com/vk_preview_D2EHEjKZub7iyGZzrceRcf16ifkfJIxrX7dlqR9g/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 Fee Optimizer capabilities are ready to use.
{
"mcpServers": {
"wine-tasting-fee-optimizer-mcp": {
"url": "https://edge.vinkius.com/vk_preview_D2EHEjKZub7iyGZzrceRcf16ifkfJIxrX7dlqR9g/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 Fee Optimizer
FAQ
Questions Wine Tasting Fee Optimizer owners ask.
- 01
How does the optimizer determine the best fee?
The get_optimal_fee_structure capability calculates the fee that maximizes the sum of the tasting fee revenue (minus costs) and the expected wine sales revenue based on conversion probabilities.
- 02
Can I account for group discounts?
Yes, use calculate_group_pricing_impact to adjust individual fee optimizations for group sizes and operational overhead.
- 03
How do I handle weekend vs weekday differences?
You can use simulate_weekday_vs_weekend_sensitivity to compare how a specific fee performs across different demand scenarios.
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