How to Use the Vectorizer AI MCP in Claude Code

Q: Can I monitor resource usage when running Vectorizer AI with Claude Code?

Yes. You run the command getaccountstatus. This shows your current subscription status and remaining credits, letting you bake resource checks right into your deployment script.

Automate asset pipelines: Vectorizer AI for Claude Code.

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Setup Vectorizer AI with Claude Code

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Running Asset Builds with the MCP Server

Need to convert a batch of PNGs during a CI run? You call `vectorize_image` from your shell script. It handles turning those bitmaps into vector formats like SVG, EPS, PDF, or DXF. Because this is headless, you immediately pipe the result through `download_image` to stage the asset for deployment.

Maintenance Tasks with Claude Code

Scripts need cleanup too. Use `delete_image` in your automation pipelines when a build generates temporary assets that must be removed after policy retention expires. Also, always verify resource availability. Running `get_account_status` ensures your CI/CD job doesn't fail unexpectedly due to credit depletion.

Batch Processing with Claude Code

You can chain operations in a single script. First, execute `vectorize_image` against the source files. Then, use `download_image` to grab all resulting vector assets and package them for the next stage of your pipeline. This allows you to treat asset generation as just another build step.

Setup guide

Set up Vectorizer 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 vectorizer-ai-mcp with a green status indicator.
3

Start using tools

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

Terminal

claude mcp add --transport http vectorizer-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 Vectorizer 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": {
    "vectorizer-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 Vectorizer 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 Vectorizer AI MCP in Claude Code

You invoke the tools via your shell script. The process looks like this: `vectorize_image` runs first, and then you use `download_image` to pull down all required vector assets for deployment.

Yes. You run the command `get_account_status`. This shows your current subscription status and remaining credits, letting you bake resource checks right into your deployment script.

The tool converts common bitmap files (JPG/PNG) into high-quality vector graphics, including SVG, EPS, PDF, and DXF. These are ideal for automated pipelines.

This MCP Server interacts with image data. It processes bitmap Images (JPG/PNG) into vectors and provides a mechanism to manually delete stored Images using `delete_image`.

Yes. The tools are built for headless execution, making them perfect for running reliable asset generation and management within Docker containers or GitHub Actions.

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