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Vinkius runs on Vercel AI SDK

How to Use the Shotstack MCP in Vercel AI SDK

Generate and stream personalized videos programmatically, right into your UI with Vercel AI SDK.

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Works with every AI agent you already use

…and any MCP-compatible client

Shotstack MCP on Cursor AI Code Editor MCP Client Shotstack MCP on Claude Desktop App MCP Integration Shotstack MCP on OpenAI Agents SDK MCP Compatible Shotstack MCP on Visual Studio Code MCP Extension Client Shotstack MCP on GitHub Copilot AI Agent MCP Integration Shotstack MCP on Google Gemini AI MCP Integration Shotstack MCP on Lovable AI Development MCP Client Shotstack MCP on Mistral AI Agents MCP Compatible Shotstack MCP on Amazon AWS Bedrock MCP Support
MCP Servers — Included with Plan
Vinkius runs on Vercel AI SDK

Connect Shotstack MCP to Vercel AI SDK

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

GDPR Included with Plan

Key Capabilities

Start a Video Render Job

You can initiate complex video generation workflows using `render_video`. This tool accepts template parameters and immediately returns a unique render ID. Your agent monitors this job status by calling `get_render_status` until the output is ready. It’s a fast loop that gives you real-time feedback on where the rendering pipeline stands.

Manage Video Assets and Outputs

When a render finishes, you need to find the resulting files. Use `list_assets_from_render` with a specific job ID to get every associated media asset. You can also check your overall inventory using `list_hosted_assets`. This gives your client total control over what inputs are used and exactly what outputs were generated by the Shotstack MCP Server.

Build a Content Pipeline

Before rendering, you need content. `ingest_media_source` lets your agent upload or fetch the raw assets necessary for the video. You can also inspect available starting points using `list_templates`. This two-step process ensures your client has both the source media and the correct template structure ready to go.

Setup guide

Set up Shotstack 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. 1

    Install dependencies

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

  2. 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. 3

    Discover and use tools

    Call mcpClient.tools() to auto-discover all Shotstack tools. Pass them directly to generateText() or streamText() — no manual schema definitions needed.

  4. 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 Shotstack transactions",
});

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

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Shotstack. 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.

Why Choose Vinkius

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Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Shotstack MCP in Vercel AI SDK

Your agent handles it by calling `render_video` first. It gets a render ID, then uses the Vercel AI SDK to stream status updates from `get_render_status`. You see the process working live, not after a loading spinner.
Yep. Just call `list_templates` to pull down every available video template name. This is crucial for letting your agent know what structural options exist before building the final render.
You should run `ingest_media_source` first. Once those assets are available, you can then pass them to the rendering process using your Shotstack MCP Server tools.
This server primarily handles `render IDs`, raw `media sources`, and final `video assets`. Your client never sees sensitive user inputs, just the media files needed for rendering.
The key here is programmatic control. The Shotstack MCP Server lets your agent interact with every step—from initial template listing (`list_templates`) to final asset retrieval (`list_assets_from_render`). It’s built for continuous, live integration.

Start using the Shotstack MCP today

We host it, we monitor it, we maintain it. You just paste one token.

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