How to Use the Cocktail API MCP in Vercel AI SDK

Stream real-time recipe data into your Vercel AI SDK interface without waiting for slow UI refreshes.

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Connect Cocktail API MCP to Vercel AI SDK

Create your Vinkius account to connect Cocktail API 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 Cocktail API with Vercel AI SDK

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Live cocktail data in Vercel AI SDK

Feed `get_classic_margaritas` and `get_classic_martinis` directly into your streamText responses. Your frontend renders the recipe steps as the data hits your serverless function. Users stop staring at blank screens while the agent thinks. The results arrive piece by piece, keeping your UI snappy and responsive.

Ingredient-based lookups with Vercel AI SDK

Trigger `get_cocktails_by_ingredients` when your application needs to suggest drinks based on what's in the pantry. This MCP Server maps your user input to exact recipe matches instantly. Your code handles the response object immediately. You get clean, structured data ready for display components without messy parsing logic.

Search and filter with Vercel AI SDK

Call `search_cocktails` to let your AI client find specific drinks by name. It bridges the gap between raw user queries and a structured database of classic recipes. This keeps your application logic thin. You offload the search index to the server and focus on building the visual components.

Setup guide

Set up Cocktail API 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 Cocktail API 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 Cocktail API 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 Cocktail API. 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 Cocktail API MCP in Vercel AI SDK

Pass the filter parameters into the tool call within your generateText function. The server returns only the recipes matching your specified criteria. It handles the heavy lifting so your code stays clean.

Yes, you can bundle multiple tool calls in a single request. Your agent will fetch several drink variations in one pass. This cuts down on latency for the end user.

It does. Since it uses standard HTTP transport, it fits right into your edge runtime. You get low-latency data without bloating your deployment size.

Wrap your tool invocation in a standard try-catch block. If the API fails to find a drink, you can return a fallback message to the user. This keeps your interface from breaking.

The server only processes recipe requests and ingredient lists. No personal user information or sensitive credentials ever touch the Cocktail API endpoints. Your data remains isolated within your application scope.

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