How to Use the Shotstack MCP in Mastra AI

Q: How does Shotstack and Mastra AI manage render status?

Mastra AI uses getrenderstatus to poll the MCP Server. This allows it to build conditional logic: if the status is 'failed,' it triggers a retry with different parameters.

Q: Can Shotstack and Mastra AI use list templates for workflow setup?

Yes, listtemplates helps your agent understand the available starting points. It gives you visibility into all possible video formats before running a critical path.

Q: Does the Shotstack MCP Server track render history for Mastra AI?

It does. The listrecentrenders tool provides a chronological list of all completed and failed jobs, which is critical for building robust failure handling logic.

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Setup Shotstack with Mastra AI

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Establish Video Templates

Start by calling `list_templates` to survey all existing video templates. If you need a new structure, use `create_video_template` to save an edit as a reusable template. This is the foundational step for any complex workflow; it ensures the rendering process always uses approved, structured designs.

Execute Reliable Renders

To run a video job, call `render_video` to get a render ID. The Mastra AI agent can then use this ID with `get_render_status` to check the progress. This structure allows your workflow engine to wait and retry if the initial render fails due to transient network issues.

Track Asset Changes

You can list all hosted files using `list_hosted_assets` or check specific details with `get_hosted_asset_details`. For a full history, the `list_recent_renders` tool shows every job executed. This comprehensive tracking is key for building reliable workflows that need an audit trail.

Setup guide

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

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

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

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Common questions about Shotstack MCP in Mastra AI

Mastra AI uses `get_render_status` to poll the MCP Server. This allows it to build conditional logic: if the status is 'failed,' it triggers a retry with different parameters.

Yes, `list_templates` helps your agent understand the available starting points. It gives you visibility into all possible video formats before running a critical path.

The MCP Server supports various media sources, including uploaded files via `ingest_media_source` and pre-existing hosted files listed by `list_hosted_assets`.

It does. The `list_recent_renders` tool provides a chronological list of all completed and failed jobs, which is critical for building robust failure handling logic.

This server touches media assets and video content. The process involves reading source material (via `ingest_media_source`) and generating final rendered files.

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