How to Use the Shotstack MCP in Claude Code

Q: How do I use Shotstack and claude-code together for automated video builds?

Your script runs through the steps: list templates, call rendervideo, loop on getrenderstatus until complete. This is a classic headless pipeline.

Q: What’s the best way to use Shotstack with claude-code for asset management?

Before rendering, you should always run listingestedsources. This command verifies that every piece of media source file is available and ready in your automated environment.

Q: If I use Shotstack with claude-code, can I track multiple jobs?

Yes. You get a unique ID for each rendervideo call. Your script just needs to manage an array of these IDs and check them individually using getrenderstatus.

Automate full video pipelines via the terminal using claude-code.

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Setup Shotstack with Claude Code

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Programmatic render initiation

Need to kick off a batch of videos from a script? Use `render_video` to get your job ID. This function initiates the entire rendering process on Shotstack, giving you a unique tracking number. You can then pipe that ID into subsequent commands for status checks or asset retrieval.

Check render status via CLI

A script needs to know if a job finished. Run `get_render_status` with the job ID you received earlier. The output tells your shell exactly whether the video is 'processing' or ready. This makes it perfect for CI/CD pipelines where you can't rely on manual checks.

List and verify assets in scripts

After a render finishes, run `list_assets_from_render` using the job ID. This command returns all associated media files so your script knows exactly what to package up. You can also check general uploaded content with `list_hosted_assets` if you're debugging a deployment.

Setup guide

Set up Shotstack MCP in Claude Code

Prerequisites

Claude Code CLI installed (npm install -g @anthropic-ai/claude-code)
Active Vinkius subscription with a valid endpoint token

1

Run the add command

Open your terminal and run the command shown on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com. Use --scope user to make it available across all projects.
2

Verify the connection

Start a Claude Code session and type /mcp to list connected servers. You should see shotstack-mcp with a green status indicator.
3

Start using tools

Ask Claude Code something like "Check my latest Shotstack transactions." It will automatically discover and invoke the available Shotstack tools.

Terminal

claude mcp add --transport http shotstack-mcp https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

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Prerequisites

Claude Code CLI installed
Active Vinkius subscription with a valid endpoint token

1

Open the config file

Create or edit .mcp.json in your project root for project-level scope, or ~/.claude.json for user-level scope.
2

Add the Shotstack MCP

Paste the JSON snippet shown on the right into the mcpServers object. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Restart Claude Code

Start a new Claude Code session. Type /mcp to confirm the server is connected. The tools will be automatically available in your conversation.

.mcp.json

{
  "mcpServers": {
    "shotstack-mcp": {
      "type": "url",
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

Get your connection token →

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Common questions about Shotstack MCP in Claude Code

Your script runs through the steps: list templates, call `render_video`, loop on `get_render_status` until complete. This is a classic headless pipeline.

Before rendering, you should always run `list_ingested_sources`. This command verifies that every piece of media source file is available and ready in your automated environment.

Yes. You get a unique ID for each `render_video` call. Your script just needs to manage an array of these IDs and check them individually using `get_render_status`.

Credentials are handled by the MCP Server's endpoint token. You pass this secure token to your CLI environment, keeping sensitive API keys out of your shell scripts.

It primarily handles structured metadata: render job IDs, template definitions, and references to uploaded media assets. The actual video file is stored externally.

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