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

Q: How does Mastra AI handle long-running tasks from Kling AI (Generative Video & Image)?

Mastra AI is built for this. After calling texttovideo, your agent polls getvideotask. The workflow engine manages the state, so it can wait minutes or hours without blocking and then resume when the task from the MCP server is complete.

Q: How would I implement human-in-the-loop approval for videos from Kling AI (Generative Video & Image) in Mastra AI?

After getvideotask succeeds, your workflow would use Mastra's requireToolApproval feature. It pauses execution, sends the video URL to a person for review, and only proceeds to the next step after getting an approval signal.

Build resilient video generation workflows with Mastra AI. Automate retries for `text_to_video` and branch on task status.

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Connect Kling AI (Generative Video & Image) MCP to Mastra AI

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Setup Kling AI (Generative Video & Image) with Mastra AI

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Claude

Perplexity

Unattended Video Generation

This isn't about firing off a single API call; it's about building a workflow that survives errors. Your Mastra AI agent calls `text_to_video` to start a job, then enters a polling loop on `get_video_task`. Mastra's engine handles the logic. If `get_video_task` fails or times out, you can configure automatic retries with exponential backoff. If it succeeds, the workflow can pass the video URL to the next step, like a notification service or a database.

Conditional E-commerce Asset Pipelines

Automate your entire product visualization pipeline. An agent can use `virtual_try_on` to generate a new product shot. The workflow then polls `get_tryon_task` to check the result. Here's where Mastra AI shines. You build conditional logic in your workflow. If the `get_tryon_task` succeeds, the agent adds the image URL to a database. If it fails, it can log the error and try again with a different model image.

A Resilient Mastra AI MCP Server

This MCP server provides all the tools for a complete generative pipeline. Your agent can start with `text_to_image` to create a character, then use `image_to_video` to animate it, all within one workflow. Each step is a separate, monitored task. Your workflow can even use `list_video_tasks` to get a history of recent jobs for auditing or cleanup. Mastra AI ensures the sequence runs reliably from start to finish.

Setup guide

Set up Kling AI (Generative Video & Image) MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Kling AI (Generative Video & Image) tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "kling-ai-generative-video-image-mcp-client",
  servers: {
    "kling-ai-generative-video-image-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Kling AI (Generative Video & Image) Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Kling AI (Generative Video & Image) tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Kling AI (Generative Video & Image) transactions"
);
console.log(result.text);

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Common questions about Kling AI (Generative Video & Image) MCP in Mastra AI

Mastra AI is built for this. After calling `text_to_video`, your agent polls `get_video_task`. The workflow engine manages the state, so it can wait minutes or hours without blocking and then resume when the task from the MCP server is complete.

Yes, that's a core use case for Mastra AI. You'd structure your workflow with conditional branching. The agent would check the status from `get_video_task` after the first attempt and execute the `text_to_video` tool only if the status is 'failed'.

Reliability. A simple script crashes on a network error or API failure. Mastra AI provides automatic retries, state management for long jobs, and conditional logic to handle unexpected outcomes from tools like `get_tryon_task`.

After `get_video_task` succeeds, your workflow would use Mastra's `requireToolApproval` feature. It pauses execution, sends the video URL to a person for review, and only proceeds to the next step after getting an approval signal.

The video portrait and audio files you provide are processed within a zero-trust, ephemeral environment. Vinkius ensures the data is encrypted in transit and at rest, and it's automatically deleted once the `get_lipsync_task` call returns the final MP4. Nothing is stored long-term.

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