How to Use the Vectorizer AI MCP in Cline

Q: Can Cline manage asset lifecycles using Vectorizer AI?

Absolutely. You can script the deletion of old files using deleteimage, ensuring your project directory stays clean and compliant with data retention policies.

Q: What status checks are possible when using Vectorizer AI with Cline?

You'll use getaccountstatus to fetch the current subscription status and count how many credits you have available before running any expensive vectorization jobs.

Q: Does Vectorizer AI support multiple output formats in Cline?

Yes. After running vectorizeimage, you can use downloadimage to pull down both the primary vector result and any supplementary file formats needed for your application.

Automate asset pipelines: Vectorizer AI for Cline.

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Connect Vectorizer AI MCP to Cline

Create your Vinkius account to connect Vectorizer AI to Cline and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Vectorizer AI with Cline

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Building Assets with Cline and MCP Server

You can incorporate vector conversion directly into your codebase. Use `vectorize_image` to programmatically convert JPG or PNG assets into clean SVG, EPS, PDF, or DXF formats. Cline lets you run this tool as part of a larger task, writing code that triggers the vectorization and then uses `download_image` to ingest the resulting asset.

Managing Project Dependencies with Cline

Cleanup isn't just for git. Use `delete_image` in your scripts when a design file is deprecated or needs manual removal from the storage policy. Need to know if you can afford this operation? Run `get_account_status`. It checks your subscription status and remaining credits, letting your script fail safely if resources run low.

Completing Asset Workflows with Cline

The workflow is simple: first, call `vectorize_image` on the bitmap source. Second, use `download_image`. This sequence gets you both the production result and any required additional formats in a single code block. This whole process helps automate asset ingestion into your project build.

Setup guide

Set up Vectorizer AI MCP in Cline

Prerequisites

VS Code with Cline extension installed
Active Vinkius subscription with a valid endpoint token

1

Open Cline MCP settings

Click the Cline icon in the VS Code sidebar to open the Cline panel. Then click the MCP Servers icon (server stack) at the top-right corner of the panel.
2

Add a remote server

Click "Remote Servers" at the top, then click "Add Remote MCP". In the Name field, type vectorizer-ai-mcp. In the URL field, paste your Vinkius endpoint: https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp. Get your token from cloud.vinkius.com.
3

Enable the server

After saving, the server appears in the Cline MCP panel. Toggle the switch to enable it. The status indicator turns green when the connection is live.
4

Start using tools

Return to the Cline chat and ask: "Check my latest Vectorizer AI refund status." Cline will discover the available tools and request your approval before invoking each one — giving you full control over every action.

Cline MCP Settings

{
  "mcpServers": {
    "vectorizer-ai-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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Common questions about Vectorizer AI MCP in Cline

You call the tools directly within your script or test suite. For example, you can use `vectorize_image` to process assets and then write logic around using `download_image` for file handling.

Absolutely. You can script the deletion of old files using `delete_image`, ensuring your project directory stays clean and compliant with data retention policies.

You'll use `get_account_status` to fetch the current subscription status and count how many credits you have available before running any expensive vectorization jobs.

The MCP Server manages image data. This includes processing bitmap Images (JPG/PNG) to create vectors and allows for the removal of stored Images using `delete_image`.

Yes. After running `vectorize_image`, you can use `download_image` to pull down both the primary vector result and any supplementary file formats needed for your application.

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