How to Use the Winevybe MCP in Vercel AI SDK

Q: How does Winevybe work with Vercel AI SDK?

You call mcpClient.tools() and pass the list of wine tools to your generator. The client then streams the resulting data directly into your frontend, letting users see the live results immediately.

Q: Can I check my cellar inventory using Winevybe and Vercel AI SDK?

Absolutely. Use getusercellar to pull an authenticated user's full wine collection. The data streams right into your app, giving you a live view of their stored bottles.

Q: Is the pairing recommendation from Winevybe stable with Vercel AI SDK?

Yes. When you use getpairings, the result appears in real time as data streams into your application, ensuring a consistent and immediate user experience.

See Winevybe results live in your UI with the Vercel AI SDK.

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Connect Winevybe MCP to Vercel AI SDK

Create your Vinkius account to connect Winevybe to Vercel AI SDK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Winevybe with Vercel AI SDK

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Real-Time Cellar Management

Use `get_user_cellar` to pull a user's current inventory list. The data streams instantly into your React or Next.js UI, showing the bottle count and types without any loading spinners. You can build an interactive cellar dashboard where users see their collection update as fast as the AI client fetches it.

Comparing Wines with Vercel AI SDK

The `compare_wines` tool lets you pull side-by-side tasting notes. When a user requests this comparison, your streaming AI response shows the contrasting stats—like grape type or body—appearing in real time. This makes for a smooth, live experience; the UI doesn't freeze while it waits for complex data.

Getting Wine Details via MCP Server

`get_wine_detail` provides deep tasting notes and stats on any given wine. Your AI client streams this text directly into the user interface, meaning the detailed profile appears word by word as it's fetched. You don't need a 'loading more' button; the data just flows out to the frontend.

Setup guide

Set up Winevybe MCP in Vercel AI SDK

Prerequisites

Node.js 18+ and a TypeScript project
ai + @modelcontextprotocol/sdk packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies

Run npm install ai @modelcontextprotocol/sdk plus your preferred model provider (e.g. @ai-sdk/openai).
2

Create the Streamable HTTP transport

Use StreamableHTTPClientTransport with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and use tools

Call mcpClient.tools() to auto-discover all Winevybe tools. Pass them directly to generateText() or streamText() — no manual schema definitions needed.
4

Works with any model provider

Swap openai("gpt-4o") for any AI SDK provider — Anthropic, Google, Mistral. The MCP tools work identically across all supported models.

index.ts

import { experimental_createMCPClient as createMCPClient } from "ai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const transport = new StreamableHTTPClientTransport(
  new URL("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
);

const mcpClient = await createMCPClient({ transport });
const tools = await mcpClient.tools();

const { text } = await generateText({
  model: openai("gpt-4o"),
  tools,
  prompt: "List recent Winevybe transactions",
});

console.log(text);
await mcpClient.close();

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Winevybe. 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.

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Common questions about Winevybe MCP in Vercel AI SDK

You call `mcpClient.tools()` and pass the list of wine tools to your generator. The client then streams the resulting data directly into your frontend, letting users see the live results immediately.

Absolutely. Use `get_user_cellar` to pull an authenticated user's full wine collection. The data streams right into your app, giving you a live view of their stored bottles.

Yes. When you use `get_pairings`, the result appears in real time as data streams into your application, ensuring a consistent and immediate user experience.

It handles complex search queries, allowing users to check regional appellation details via `get_region_info` or find specific bottles using `search_wines`.

This server primarily handles structured text data, specifically detailed wine tasting notes and stats provided by the `get_wine_detail` tool.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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