How to Use the Modelbit (ML Model Deployments) MCP in Vercel AI SDK

Stream real-time ML inference results directly into your Vercel AI SDK frontends with the Modelbit MCP Server.

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Connect Modelbit (ML Model Deployments) MCP to Vercel AI SDK

Create your Vinkius account to connect Modelbit (ML Model Deployments) 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 Modelbit (ML Model Deployments) with Vercel AI SDK

Ask AI about this MCP

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Claude

Perplexity

Run live model predictions in the browser

Trigger your deployed ML models instantly by calling `get_inference`. The data flows from Modelbit straight into your streaming response, cutting out the typical wait time for backend processing. Your UI updates as the inference completes. This keeps the user experience snappy and responsive, avoiding those annoying loading spinners that kill conversion rates.

Connect Modelbit to your edge functions

The Modelbit MCP Server runs perfectly within your Vercel Edge Function environment. You get low-latency access to your production models without needing to manage heavy infrastructure. Define your tool call inside `streamText` and watch your app handle complex logic on the fly. It's just a standard call that returns the precise output your interface needs to display.

Manage model inputs via TypeScript

Since this is built for the Vercel AI SDK, you get full type safety when passing data to `get_inference`. Stop guessing what your model expects and start coding against a defined schema. Keep your project clean by handling the authentication and endpoint logic inside the server wrapper. That's it, you're ready to push production-grade features to your users.

Setup guide

Set up Modelbit (ML Model Deployments) 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 Modelbit (ML Model Deployments) 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 Modelbit (ML Model Deployments) transactions",
});

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

Get your connection token →

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Common questions about Modelbit (ML Model Deployments) MCP in Vercel AI SDK

Install the MCP client package and configure the HTTP transport URL. Once connected, pass the `get_inference` tool into your agent's tool list for immediate model access.

Yes. The tool result flows back through the stream, allowing your React or Next.js components to render the output the moment it arrives from the endpoint.

It does. You provide your endpoint token when initializing the client, and the server manages the handshake so your code stays focused on the inference logic.

It uses an ephemeral, zero-trust connection. Your data only moves between your client and the specific model endpoint you target.

You can send any valid JSON payload required by your model. The tool handles the serialization and passes it to your hosted inference endpoint.

Use it with your favorite AI tools

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