How to Use the Jebbit MCP in Vercel AI SDK

Q: How do I stream active experiences in my Vercel AI SDK app with Jebbit?

You use the listexperiences tool inside your model's tool call loop. The Vercel AI SDK handles the stream, allowing your UI to render the active survey names and publication status in real-time.

Q: Can I retrieve specific zero-party attributes using Jebbit and Vercel AI SDK?

Yes, your agent can call listattributes to fetch all captured customer preferences. The Vercel AI SDK streams these key-value pairs directly into your React components for immediate rendering.

Q: How does Vercel AI SDK handle Jebbit account validation?

The agent invokes getaccount via the SDK's tool calling mechanism to check your subscription and status. This happens securely on the Edge, ensuring your credentials are never exposed to the client side.

Q: What happens if a reporting job takes too long to complete?

Your agent can run listreportingjobs to monitor the status of large data exports. This prevents your Edge function from timing out while waiting for a massive payload to build.

Build real-time Next.js interfaces that stream zero-party quiz data using this Jebbit MCP Server and Vercel AI SDK.

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…and any MCP-compatible client

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Connect Jebbit MCP to Vercel AI SDK

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

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Stream live quiz structures to your Next.js UI

Your Vercel AI SDK edge functions render interactive survey layouts dynamically by querying `get_experience` or `list_experiences` from Jebbit. The streaming agent grabs active quiz schemas and injects them directly into the React tree without page reloads. This setup avoids loading spinners. Because this MCP connection handles the stream directly, your model can instantly build custom UI components matching the live publication status of your Jebbit experiences.

Map consumer attributes instantly via this MCP Server

Feed your Vercel AI SDK streaming UI with real-time zero-party data points fetched from Jebbit. When your agent runs `list_attributes`, it instantly identifies captured customer preferences like favorite colors or purchase intent to update the user's view on the fly. This bypasses complex backend polling. Because the streaming connection handles the flow directly, the model can instantly display personalized recommendations based on the active attributes it finds in your Jebbit account.

Verify live integration flows on the Edge

Check where your Jebbit lead data is going without leaving your Vercel AI SDK chat interface. Your streaming agent uses `list_integrations` to verify that active marketing pipelines are processing user responses correctly. If a user reports a broken sync, the Vercel AI SDK model runs this tool to inspect the connection state. It outputs the status directly to your admin panel, helping you debug Jebbit API connections in milliseconds.

Setup guide

Set up Jebbit 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 Jebbit 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 Jebbit 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 Jebbit. 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 Jebbit MCP in Vercel AI SDK

You use the `list_experiences` tool inside your model's tool call loop. The Vercel AI SDK handles the stream, allowing your UI to render the active survey names and publication status in real-time.

Yes, your agent can call `list_attributes` to fetch all captured customer preferences. The Vercel AI SDK streams these key-value pairs directly into your React components for immediate rendering.

The agent invokes `get_account` via the SDK's tool calling mechanism to check your subscription and status. This happens securely on the Edge, ensuring your credentials are never exposed to the client side.

Your agent can run `list_reporting_jobs` to monitor the status of large data exports. This prevents your Edge function from timing out while waiting for a massive payload to build.

All consumer attributes and survey responses retrieved via this MCP server are processed in ephemeral V8 sandboxes. Your Vercel AI SDK setup only accesses this data in memory during the active session, with no persistent storage on our servers.

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