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How to Use the Twelve Labs (Video Understanding) MCP in Mastra AI

Build resilient video data pipelines with Mastra AI and our MCP Server.

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Connect Twelve Labs (Video Understanding) MCP to Mastra AI

Create your Vinkius account to connect Twelve Labs (Video Understanding) to Mastra AI 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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Analyze videos asynchronously

If you're processing a massive backlog of footage, don't wait on synchronous calls. The `analyze_async` tool handles this heavy lifting in the background and reports progress. This is perfect for your complex workflows: trigger the analysis via Mastra AI, let it run, and then use other steps to check the status until the results are ready.

Build structured knowledge indexes

You don't just want raw data; you need structure. Use `create_index` to define a schema for your video insights, and then use `list_indexes` to see what's already set up. This allows Mastra AI to treat the resulting video analysis as highly organized records, making subsequent steps reliable and predictable.

Handle large file uploads reliably

When dealing with gigabytes of footage, you can't just send one massive request. `create_multipart_upload` manages the session needed to upload content in chunks. This ensures that even if a connection drops mid-transfer, your workflow has the necessary tools (`report_multipart_progress`) to pick up right where it left off.

Setup guide

Set up Twelve Labs (Video Understanding) 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. 1

    Install dependencies

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

  2. 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. 3

    Discover and inject tools

    Call mcpClient.listTools() and spread the result into your agent's tools object. All Twelve Labs (Video Understanding) tools become native Mastra tools.

  4. 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: "twelve-labs-video-understanding-mcp-client",
  servers: {
    "twelve-labs-video-understanding-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Twelve Labs (Video Understanding) Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Twelve Labs (Video Understanding) tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Twelve Labs (Video Understanding) transactions"
);
console.log(result.text);

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Twelve Labs. 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 Twelve Labs (Video Understanding) MCP in Mastra AI

You use the `analyze_async` tool. Since Mastra AI handles retries, if the initial processing fails for any reason, your workflow will automatically retry until the segmentation is complete.
You define a schema with `create_index` and then populate it by calling `index_asset`. This structured approach guarantees that your downstream workflows are always working with predictable data.
The server touches raw video content, multipart upload session details, generated embeddings, and structured index definitions. These are all managed by your agent framework.
Yeah, it does. The `create_multipart_upload` tool handles chunking, making sure you can process massive videos without running into upload limits.
You check the progress reports using `report_multipart_progress`, confirming that all parts have been successfully uploaded and processed before advancing your workflow.

Start using the Twelve Labs (Video Understanding) MCP today

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