How to Use the Dataiku DSS MCP in Vercel AI SDK

Q: Can Dataiku DSS jobs be tracked in a Vercel AI SDK dashboard?

Yes, use listjobs to pull the latest status. You can map these outputs to a progress bar or status indicator in your frontend.

Stream Dataiku DSS metadata directly into your Vercel AI SDK interface for live, real-time data science monitoring in your web app.

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Connect Dataiku DSS MCP to Vercel AI SDK

Create your Vinkius account to connect Dataiku DSS 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 Dataiku DSS with Vercel AI SDK

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Inspect Dataiku DSS schemas in Vercel AI SDK

Feed your UI actual column names and data types by calling `dataset_schema`. You'll stop guessing what's inside your tables and start building components that react to the data structure. This MCP Server provides the raw metadata, and your edge functions handle the rest. Your users see the dataset layout the moment the agent finishes its check.

Trigger pipelines from your Vercel AI SDK frontend

Fire off `run_scenario` directly from a button click inside your React or Svelte app. It takes the heavy lifting of backend orchestration and puts it right into the user's hands. Your app handles the state transitions while the server executes the job. You'll see the status updates flow back through the stream without a single page refresh.

Monitor model performance with Vercel AI SDK

Query your saved model metrics using `get_model` to display live performance charts in your dashboard. You aren't just looking at static values; you're pulling live stats into your frontend. Everything stays connected through the stream. Your users get immediate visibility into how their models are performing without leaving your application.

Setup guide

Set up Dataiku DSS 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 Dataiku DSS 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 Dataiku DSS transactions",
});

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

Get your connection token →

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Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

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Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Dataiku DSS MCP in Vercel AI SDK

You initialize the client using the Vercel AI SDK transport utility. Point it at your Vinkius endpoint, and the tools become available for your agents to call.

Yes, use `list_jobs` to pull the latest status. You can map these outputs to a progress bar or status indicator in your frontend.

It does. The tools are designed to return data that fits right into your streamText calls. The user sees the output as it arrives from the server.

Your API key is handled by the Vinkius sandbox. Only the ephemeral token you generate ever touches your client code.

The server only touches project metadata, dataset schemas, and job logs. It never accesses raw row-level data unless you explicitly pipe it through a tool.

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