How to Use the Groq MCP in Vercel AI SDK

Get LPU-accelerated responses in your Vercel AI SDK projects with this Groq MCP Server.

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Connect Groq MCP to Vercel AI SDK

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

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Real-time chat completions with Groq

Use the `chat_completion` tool to pipe Groq's high-throughput inference directly into your streaming UI. Your frontends won't hang waiting for tokens. Connect this MCP Server to your Edge Functions and watch the text render as it arrives. It's the fastest way to get Llama or Mixtral data into your browser.

Structured JSON for Vercel AI SDK

Call `structured_output` to force the model to return clean, typed JSON objects. You don't have to deal with messy string parsing in your React components. It handles the schema validation internally. You get the data in the exact format your state management needs.

Audio processing in the browser

Feed user audio files into `transcribe_audio` or `translate_audio` and pipe the result into your stream. It turns raw voice input into text instantly. This keeps your audio pipeline logic off your main thread. Just send the blob to the server and handle the text output as it streams back.

Setup guide

Set up Groq 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 Groq 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 Groq transactions",
});

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

Get your connection token →

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Common questions about Groq MCP in Vercel AI SDK

You initialize the client via the MCP transport and pass the tool definitions into your `streamText` call. It links the Groq inference engine to your existing AI SDK workflow.

Yes, the transport layer is designed to run in lightweight environments. You'll see low-latency performance even when deploying your app to the edge.

Absolutely. You can mix and match tools from various sources within your agent. The SDK handles the aggregation so you can call `chat_completion` alongside your own local functions.

The server exposes standard error codes that your client can catch. You should implement a standard try-catch block around your `generateText` calls to handle any API-side issues.

We don't store your input logs or audio buffers on our servers. Your data moves directly from the Vercel environment to the Groq API endpoint over a secure, ephemeral connection.

Use it with your favorite AI tools

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

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