How to Use the Kling AI (Generative Video & Image) MCP in Vercel AI SDK

Q: How do I use Kling AI (Generative Video & Image) with the Vercel AI SDK to show video progress?

Call texttovideo to get a task ID. Then, use a polling function in your React component to repeatedly call getvideotask with that ID. You can bind the status from the response directly to your UI to show a live progress indicator.

Q: Can I generate images and videos at the same time?

Yes. Each call to texttovideo or texttoimage initiates a separate, asynchronous job. You'll get a unique task ID for each, and you can poll getvideotask and getimagetask independently.

Q: What's the best way to handle task failures with Kling AI (Generative Video & Image) in the Vercel AI SDK?

The getvideotask and other polling tools will return a 'failed' status in their response. In your frontend code, check for this status and display a user-friendly error message. Since it's an asynchronous process, the main app experience isn't blocked.

Q: Is the Kling AI (Generative Video & Image) MCP Server a good fit for a Vercel AI SDK project needing lip-sync?

Absolutely. You can use the lipsyncvideo tool to submit a video and audio file. Poll the getlipsynctask endpoint until it succeeds, then stream the final MP4 URL directly into your video player component.

Stream generated videos and virtual try-on results directly into your React components with the Vercel AI SDK.

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Setup Kling AI (Generative Video & Image) with Vercel AI SDK

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Live Video Generation in Your UI

Kick off a video generation job right from your frontend. The `text_to_video` tool takes a user's prompt and immediately returns a task ID, so you can show progress instead of just a spinner. You'll then poll the `get_video_task` endpoint in the background. Once the status flips to 'succeed', the final MP4 URL streams directly into your Next.js or Svelte component. It's a live generation experience, not a 'check back later' page.

Build Interactive Virtual Try-Ons

This is how you build a modern e-commerce page. Your user clicks a button, and your app calls the `virtual_try_on` tool to map a product image onto a model photo. It's one API call from your frontend. While Kling AI works, you can use the Vercel AI SDK to stream status updates from `get_tryon_task`. When the composited image is ready, its URL appears in your UI instantly. No page reloads, just a smooth, interactive try-on.

Your Vercel AI SDK Media Engine

Animate static product shots or user-uploaded images. The `image_to_video` tool gets you a task ID, and your UI can track the job's progress by polling `get_video_task`. It's not just video. Generate product concepts with `text_to_image` and check its status with `get_image_task`. This MCP server gives your UI direct access to a full generative media pipeline, all managed through a single MCP connection.

Setup guide

Set up Kling AI (Generative Video & Image) 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 Kling AI (Generative Video & Image) 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 Kling AI (Generative Video & Image) 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 Kling AI. 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 Kling AI (Generative Video & Image) MCP in Vercel AI SDK

Call `text_to_video` to get a task ID. Then, use a polling function in your React component to repeatedly call `get_video_task` with that ID. You can bind the status from the response directly to your UI to show a live progress indicator.

Yes. Each call to `text_to_video` or `text_to_image` initiates a separate, asynchronous job. You'll get a unique task ID for each, and you can poll `get_video_task` and `get_image_task` independently.

The `get_video_task` and other polling tools will return a 'failed' status in their response. In your frontend code, check for this status and display a user-friendly error message. Since it's an asynchronous process, the main app experience isn't blocked.

Absolutely. You can use the `lip_sync_video` tool to submit a video and audio file. Poll the `get_lipsync_task` endpoint until it succeeds, then stream the final MP4 URL directly into your video player component.

Your source garment and model images are sent to the MCP server for processing. They exist only for the duration of the `virtual_try_on` task and are purged from ephemeral storage after the final composite image is generated. Vinkius isolates each request in a sandbox.

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