How to Use the Vadoo AI MCP in Claude Code

Q: How do I use Vadoo AI with Claude Code?

You connect the server via command line using claude mcp add. After that, you call the tools directly from your shell scripts or CI/CD environment.

Build CI/CD pipelines that generate video with Claude Code.

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Setup Vadoo AI with Claude Code

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Batch process and schedule media generation.

Use `vadooai_generate_podcast` in a cron job script. This lets you automatically create podcasts from source material without manual intervention—perfect for weekly content drops. You can also use this to generate videos, ensuring your pipeline always has fresh assets ready for deployment.

Automate asset creation and verification.

A solid CI/CD step involves generating a video (`vadooai_generate_video`) and then verifying its status using `vadooai_check_generation_status`. This makes your pipeline reliable because it waits for confirmation. The next step can be to run `vadooai_create_clips` on the output, making ready-to-use social media assets automatically.

Create structured content streams.

Need captions attached to a video? Running `vadooai_generate_caption` as part of a build script ensures that every deployed piece has its text transcript ready. It's just one command away. If you need to generate multiple assets, chaining these calls within your shell script is straightforward.

Setup guide

Set up Vadoo AI 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 vadoo-ai-mcp with a green status indicator.
3

Start using tools

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

Terminal

claude mcp add --transport http vadoo-ai-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 Vadoo AI 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": {
    "vadoo-ai-mcp": {
      "type": "url",
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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 Vadoo AI. 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 Vadoo AI MCP in Claude Code

You connect the server via command line using `claude mcp add`. After that, you call the tools directly from your shell scripts or CI/CD environment.

Yes. Since it supports headless execution, you can build multi-step pipelines—for example, generate video, wait for status, then create clips—all within a single script.

It handles media assets and associated metadata. This includes job statuses, generated captions, raw video files, and specific clip identifiers for your pipelines to process.

Absolutely. You can build robust CI/CD steps that poll the server using `vadooai_check_generation_status` before moving on, guaranteeing assets are ready for deployment.

The server touches media assets and associated metadata. Running through the terminal ensures that all handling of this content is done in a controlled, auditable environment.

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