Sommelier MCP for AI. Match food to wine, instantly.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
Wine Pairing & Sommelier provides expert culinary advice, letting your AI agent act like a trained sommelier. You can discover ideal pairings for any dish or ingredient, get deep flavor profiles on specific wine types, and find top-rated bottle recommendations with prices.
What your AI can do
Get dish for wine
Finds specific recipes or meals designed to pair well with a given wine type (e.g., Pinot Noir).
Get wine description
Retrieves detailed information covering the flavor profile, origin, and characteristics of any recognized wine variety.
Get wine pairing
Generates expert pairing suggestions for a dish or ingredient, including specific product recommendations with prices and ratings.
Input a specific bottle type (like Chardonnay) and receive several detailed, complementary meal suggestions.
Provide an ingredient or main course and get precise recommendations for the perfect wine match, including product links.
Input any recognized wine variety to retrieve its full flavor profile, origin story, and typical serving style.
Get immediate recommendations for specific bottles, complete with star ratings and current retail prices.
Ask an AI about this
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Wine Pairing & Sommelier: 4 Tools Available
Use these four specialized tools to build complex culinary automations. Find pairings, describe wines, and recommend bottles all through your agent.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Wine Pairing & Sommelier on VinkiusGet Dish For Wine
Finds specific recipes or meals designed to pair well with a given wine type (e.g., Pinot Noir).
Get Wine Description
Retrieves detailed information covering the flavor profile, origin, and...
Get Wine Pairing
Generates expert pairing suggestions for a dish or ingredient, including specific...
Recommend Wines
Provides targeted lists of wine products based on criteria you set, showing current...
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Wine Pairing & Sommelier, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Spoonacular Wine. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
VINKIUS INFRASTRUCTURE
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Sandboxed per request
Zero-Trust Proxy
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 4 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Cross-referencing flavors feels like pulling teeth today.
Right now, if you want to know what pairs with a dish, you open one browser tab for recipes. Then you jump to another site to check the wine's acidity and tannin levels. You copy that data into a third spreadsheet just to see which region matches up. It’s a massive amount of clicking and manually checking multiple databases.
With this MCP, you send all those variables—the dish, the required flavor profile, the budget range—to your agent in one go. The system does the cross-referencing instantly, giving you not just a suggestion, but specific product links, ratings, and prices.
Get Wine Pairing & Sommelier MCP: Specific pairing data when you need it.
You no longer have to open three different windows just to verify a pairing. You give the agent the core ingredients, and it runs the checks—checking for acidic contrast, checking tannin compatibility, and even pulling in specific bottle suggestions like Domaine Drouhin Oregon Pinot Noir 2021.
The result is immediate, highly detailed, and actionable. It’s less research time; it's more writing time.
What your AI can actually do with this
This MCP turns your AI client into an instant beverage consultant, helping you build menus, write content, or plan dinner parties around perfect food and drink matches. Instead of cross-referencing three different databases to match acidity levels with tannin structure, you simply ask the question. It handles pairing suggestions for everything from grilled salmon to pasta dishes, giving you specific bottle recommendations, ratings, and prices right away.
If your workflow involves combining this MCP’s pairings data with a billing system or inventory check, remember that Vinkius ensures all credentials pass through a zero-trust proxy. This means your key never sits on a disk while the AI agent runs complex cross-platform automations for you.
It covers more than just matching: it details wine profiles—aroma notes, origin, and style—so you understand why something works. It's essential gear for anyone building any kind of culinary or lifestyle application.
019d7622-c457-73ee-a6f6-9386d65eeecb Here's how it actually works
The bottom line is you stop guessing what works. You just ask the MCP to tell you.
Start by telling the MCP what you're working with: a dish (e.g., steak) or a wine (e.g., Malbec).
The agent sends this input to the specialized tools, which cross-reference global culinary data and flavor chemistry rules.
You get back an actionable list of perfect pairings, product suggestions, or detailed flavor breakdowns, ready for your final output.
Who is this actually for?
Food writers, restaurant managers, and beverage distributors need this. If your job requires matching complex flavor profiles or designing multi-course menus, this saves hours of research.
Designs a full meal service by inputting the primary wine choice first, then using the MCP to automatically generate compatible dishes and side pairings.
Needs instant expertise to write articles like 'Pairing Guide for Tuscany.' The agent pulls detailed descriptions for any wine type or pairing suggestion with a single prompt.
Builds automated recommendation engines that guide customers through the drink menu, ensuring every suggested bottle has clear pricing and matching food options.
What Changes When You Connect
Stop guessing pairings. Use get_wine_pairing to tell your agent a dish and immediately receive specific product suggestions with prices and ratings.
Don't waste time cross-referencing flavor charts. With get_wine_description, you pull up the complete story—from origin to tasting notes—for any wine type instantly.
Reverse engineer menus. Need a pairing for that expensive bottle? Use get_dish_for_wine and it suggests perfect meals based on the wine's structure.
Keep costs in check. The MCP can find specific bottles using recommend_wines, giving you immediate pricing data to make sure your recommendation is profitable.
Build complex workflows by chaining this MCP with others. For example, pairing a meal (using get_wine_pairing) and then generating a blog post about it.
See it in action
Designing a high-end tasting menu
A restaurant manager inputs the main courses for three different dishes. The agent uses get_wine_pairing multiple times to ensure that each course is matched with an appropriate wine, creating a seamless and profitable pairing list.
Writing educational content on terroir
A food writer needs detailed background info for a piece about Italian reds. They use get_wine_description to pull up the exact profile of Sangiovese, ensuring their article is technically accurate and rich with detail.
Planning a dinner party budget
A host knows they are serving roasted lamb. They ask the agent to find suitable pairings using get_wine_pairing, then use recommend_wines to narrow down the options based on their total spending limit.
Adjusting a recipe for pairing
A chef has developed a new spicy curry. They input 'spicy Indian curry' into get_wine_pairing, and the agent suggests that a crisp, high-acidity white wine will best counteract the heat.
The honest tradeoffs
Asking for vague pairings
Just asking 'what pairs with beef?' is useless. It gives you too many options and no specific guidance.
Be precise: Use get_wine_pairing by specifying the cut of meat, like 'grilled ribeye,' or tell it what wine you have first.
Ignoring price/availability
Getting a perfect pairing suggestion, only to find out the recommended bottle is out of budget or discontinued.
Always follow up with recommend_wines. This tool uses current market data and ratings to keep your recommendations actionable.
Treating it like simple recipe lookup
Assuming a pairing is just about 'red food, red wine.' That ignores acidity and tannin structure.
Use get_wine_pairing. It considers chemical compatibility (acidity/tannin) first, which is much more sophisticated than basic color matching.
When It Fits, When It Doesn't
You need this MCP if your process involves multi-variable decision-making—matching a physical item (a dish or wine) to another complementary item while also accounting for commercial data like price and availability. Use it when you need the why behind the pairing, not just the suggestion. If all you need is general knowledge, like 'what are three dishes that go with Malbec,' you might be okay without it. But if you need specific recommendations tied to real-world pricing, or if you're building a system where the AI needs to chain together finding a dish, then pairing the wine (using get_wine_pairing) is the only way to guarantee accuracy and completeness.
Questions you might have
How do I use the get_wine_pairing tool with a recipe? +
Just input the dish or main ingredient into get_wine_pairing. The MCP will give you expert suggestions and help pinpoint specific bottles that match your menu's vibe.
What if I already own a bottle of wine? Can I use get_dish_for_wine? +
Yes, absolutely. Use get_dish_for_wine. You tell it the wine you have—say, a Riesling—and it sends back several perfect recipes that complement those specific flavors.
Can I find out about a type of wine without pairing it? +
You can use get_wine_description. Just tell the MCP the name (like 'Sauvignon Blanc') and it pulls up all the detailed information on its aroma, origin, and style.
Does recommend_wines account for my budget? +
Yes. When you use recommend_wines, you can provide your target price range, and the MCP filters the results to only show bottles that fit both your palate and your wallet.
If I give vague inputs, how does the `get_wine_pairing` tool refine its suggestions? +
The pairings rely heavily on detail. While simple items work, giving context—like 'seared scallops with lemon' instead of just 'scallops'—provides specific flavor cues that dramatically improve accuracy.
When I use `get_wine_description`, is my query data stored or used for training? +
No. Your queries are processed through Vinkius’ zero-trust proxy and are never stored on disk. We process your wine information securely, maintaining absolute data privacy across all tools.
Are there rate limits when calling `recommend_wines` for large batches of product checks? +
The MCP manages standard API limitations automatically. For high-volume tasks, you'll need to implement controlled calls or batch processing logic within your agent workflow.
Does `get_dish_for_wine` require a full recipe, or can I just input the wine type? +
You should provide comprehensive descriptions. While you can reference the wine itself, adding details about how it's served (e.g., 'served with crusty bread') helps create more accurate dish suggestions.
What wines are covered? +
The database covers all major wine varieties including Cabernet Sauvignon, Merlot, Pinot Noir, Chardonnay, Sauvignon Blanc, Riesling, Malbec, Prosecco, Champagne, and dozens more from wine regions worldwide.
We've already built the connector for Sommelier. Just plug in your AI agents and start using Vinkius.
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