How to Use the CocktailFyi MCP in Vercel AI SDK

Get drink recipes and bartending techniques directly inside your Vercel AI SDK app, streaming data live to your UI components.

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Connect CocktailFyi MCP to Vercel AI SDK

Create your Vinkius account to connect CocktailFyi 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 CocktailFyi with Vercel AI SDK

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Stream cocktail data to your UI

The `get_cocktail` and `list_cocktails` tools fetch precise drink specs including ABV, calories, and instructions. Your Vercel AI SDK frontend receives this data in real-time, letting you render recipe cards that populate as the model processes the request. Stop waiting for full page loads or static JSON dumps. By piping these tool results into your streamText calls, you show the user the drink details the moment they hit the wire.

Search the CocktailFyi database

Trigger `search_cocktails` to let users find drinks by name or specific ingredients. This MCP Server handles the filtering logic, returning matches that you can immediately display in your React components. It keeps the search logic off your main thread. You just handle the output and let your users browse through the library.

Access deep bartending knowledge

Use `list_techniques` and `list_glossary` to inject professional bartending advice into your application. These tools provide the how-to logic for your users to refine their drink-making skills. Your app becomes an expert guide rather than just a static recipe book. You gain access to the same professional data structures used by industry pros.

Setup guide

Set up CocktailFyi 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 CocktailFyi 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 CocktailFyi transactions",
});

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by CocktailFyi. 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 CocktailFyi MCP in Vercel AI SDK

You initialize the client via your endpoint and pass the tools to your model. The AI SDK handles the execution of the tools and streams the result directly into your component state.

Yes, every cocktail object returned by the server includes specific nutritional data like calories and ABV. You can map these fields directly to your frontend UI components.

You just need your endpoint token to connect the server. Once the connection is active, your app calls the functions as standard asynchronous requests.

The server only processes your specific requests to return recipe data. No personal identifiers or user session history are stored during these lookups.

It parses your input against the ingredient database and returns precise details. Your data remains isolated within the ephemeral sandbox during the request lifecycle.

Use it with your favorite AI tools

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

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