How to Use the RenderMe MCP in Vercel AI SDK

Q: How do I stream RenderMe video generation status to a Vercel AI SDK frontend?

You use streamText along with the tools returned by mcpClient.tools(). When the agent executes createvideorenderjob, it starts a polling loop using getrenderjobstatus and yields the updates. This sends live rendering progress directly to your React or Next.js components.

Q: How does the RenderMe server handle authentication with Vercel AI SDK?

It integrates with the authProvider option in your MCP client setup. This allows you to securely pass user-specific OAuth tokens, ensuring that when the agent calls getcurrentuser or listvideoprojects, it only accesses the specific account linked to that user session.

Q: What is the correct way to close RenderMe connections in Vercel AI SDK?

Always call mcpClient.close() once your model finishes its generation run. Leaving connections open can leak resources in serverless or edge environments, so clean up immediately after the render job is queued.

Q: How does RenderMe protect my uploaded video assets and templates?

All asset metadata retrieved via listuploadedassets and template configurations fetched with gettemplatedetails are isolated within a V8 sandbox on Vinkius. Your actual video files are never stored on our servers; we only pass secure, temporary URLs to the video engine to compile your renders.

Trigger and track video renders in real-time inside your Next.js app using the Vercel AI SDK.

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Setup RenderMe with Vercel AI SDK

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Real-time render progress in your Vercel AI SDK UI

This MCP Server exposes `get_render_job_status` to stream live rendering progress directly into your user interface. Stop making users stare at a static loading spinner while their video renders on the back end. When your agent triggers a render with `create_video_render_job`, the update loop feeds directly into `streamText`. Your users see the exact percentage of progress as it happens, keeping them in the loop without constant manual page refreshes.

Instant asset and template discovery

Give your front-end agent immediate access to your entire video asset library without writing custom API wrappers. By exposing `list_uploaded_assets` and `list_video_templates` to your Vercel AI SDK runtime using this MCP Server, the agent can instantly show users which media files are ready for production. Users can select a template in your UI, and the agent uses `get_template_details` to map raw user inputs to the exact fields the video template expects. No more guess-and-test form filling.

Secure Edge-compatible video pipelines

Run your video generation pipelines right on the edge using Vercel AI SDK. You configure the server connection using `createMCPClient` inside your Next.js Edge route, keeping latency low and avoiding cold starts. The agent checks `check_api_health` before initiating a render, ensuring your pipeline is active. Once verified, it kicks off the job and safely closes the connection with `mcpClient.close()` to free up resources.

Setup guide

Set up RenderMe 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 RenderMe 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 RenderMe transactions",
});

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by RenderMe. 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 RenderMe MCP in Vercel AI SDK

You use `streamText` along with the tools returned by `mcpClient.tools()`. When the agent executes `create_video_render_job`, it starts a polling loop using `get_render_job_status` and yields the updates. This sends live rendering progress directly to your React or Next.js components.

Yes, you control this during client setup. Instead of passing all tools from `mcpClient.tools()` to your model, you can filter the array in TypeScript to expose only read tools like `list_video_templates` or write tools like `create_video_render_job` based on the user's role.

It integrates with the `authProvider` option in your MCP client setup. This allows you to securely pass user-specific OAuth tokens, ensuring that when the agent calls `get_current_user` or `list_video_projects`, it only accesses the specific account linked to that user session.

Always call `mcpClient.close()` once your model finishes its generation run. Leaving connections open can leak resources in serverless or edge environments, so clean up immediately after the render job is queued.

All asset metadata retrieved via `list_uploaded_assets` and template configurations fetched with `get_template_details` are isolated within a V8 sandbox on Vinkius. Your actual video files are never stored on our servers; we only pass secure, temporary URLs to the video engine to compile your renders.

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