How to Use the RenderMe MCP in Mastra AI

Q: How do I register RenderMe MCP Server tools with a Mastra AI agent?

Install the Mastra MCP package and initialize a new MCPClient pointing to your Vinkius endpoint. Call mcpClient.listTools() and spread those tools directly into your agent's tool configuration array.

Q: Can I trigger a Mastra AI workflow when a RenderMe video finishes rendering?

Yes, by setting up webhooks. Use listconfiguredwebhooks to see your active listeners, and configure your endpoint to trigger a Mastra workflow that runs when the render status transitions to complete.

Q: Where is my RenderMe user profile data stored when using Mastra AI?

User profile data returned by getcurrentuser and active webhook endpoints listed via listconfiguredwebhooks are processed strictly in-memory within Vinkius's zero-trust MCP sandbox. No personal information or account configuration details are cached or stored on disk, keeping your rendering credentials completely private.

Build resilient, auto-retrying video workflows with Mastra AI and RenderMe.

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Connect RenderMe MCP to Mastra AI

Create your Vinkius account to connect RenderMe to Mastra AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup RenderMe with Mastra AI

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Resilient render pipelines with Mastra AI

This MCP Server exposes `create_video_render_job` to build auto-retrying video workflows that handle rendering failures. Video rendering fails sometimes because of bad assets or API limits, but your workflow shouldn't crash. If a job fails, the Mastra agent doesn't just stop. It can check `get_account_render_stats` to see if you hit a limit, or use `check_api_health` to verify the connection before trying the render again.

Multi-step asset verification workflows

Before triggering an expensive render job, your Mastra AI agent can run a validation loop. Connecting this MCP Server lets you run automated asset checks directly inside your workflow engine. The agent calls `list_uploaded_assets` to ensure the required background files are present, organizing them with `list_asset_folders` if things are messy. If a file is missing, the Mastra workflow branches and alerts your admin.

Human-in-the-loop template approvals

This MCP Server exposes `get_template_details` to build preview parameters for human approval before execution. Don't let an agent render videos without a sanity check. You can configure Mastra's `requireToolApproval` on critical tools like `create_video_render_job` to hold the process until a human approves. The agent uses template metadata to build a preview of the video parameters and sends it to your dashboard.

Setup guide

Set up RenderMe 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 RenderMe 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: "renderme-mcp-client",
  servers: {
    "renderme-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent RenderMe transactions"
);
console.log(result.text);

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 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 Mastra AI

Install the Mastra MCP package and initialize a new `MCPClient` pointing to your Vinkius endpoint. Call `mcpClient.listTools()` and spread those tools directly into your agent's tool configuration array.

Yes, by setting up webhooks. Use `list_configured_webhooks` to see your active listeners, and configure your endpoint to trigger a Mastra workflow that runs when the render status transitions to complete.

Mastra's built-in workflow engine allows you to define retry policies. If calling `create_video_render_job` throws a rate-limit error, the engine automatically backs off and retries, checking `get_render_job_status` once the API is clear.

Yes, the MCP client automatically detects and handles both Streamable HTTP and SSE transports. You don't have to write any transport-specific code to connect Mastra to the video server.

User profile data returned by `get_current_user` and active webhook endpoints listed via `list_configured_webhooks` are processed strictly in-memory within Vinkius's zero-trust MCP sandbox. No personal information or account configuration details are cached or stored on disk, keeping your rendering credentials completely private.

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