How to Use the Vectorizer AI MCP in Cursor

Q: How do I integrate Vectorizer AI with Cursor?

You add the server to your .cursor/mcp.json file, then enable Agent mode in chat. The agent can then call vectorizeimage as if it were a native function.

Q: Can Vectorizer AI check API credits when using Cursor?

Yes, use getaccountstatus. This tool checks your subscription status and remaining usage limits before you run code that requires heavy vectorization processing.

Q: Does Vectorizer AI require manual setup in Cursor?

You must define the server configuration in a local .cursor/mcp.json file and ensure Agent mode is active for the tools to be accessible during coding.

Q: If I delete an image using Vectorizer AI on Cursor, what data is affected?

The deleteimage tool removes the specific bitmap image token. This cleans up your project scope and prevents unnecessary resource usage.

Inject vector graphics data directly into your code using Cursor and Vectorizer AI.

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Connect Vectorizer AI MCP to Cursor

Create your Vinkius account to connect Vectorizer AI to Cursor 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 Cursor

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ChatGPT

Claude

Perplexity

Vectorize Bitmaps in Code

Call `vectorize_image` when you need to convert a bitmap image (PNG, JPG) into vector data structures. The agent generates working code that calls this tool and receives the output directly as a variable. This lets you process graphical assets right inside your editor without switching context or using stubs.

Handle Image Downloads in Code

When conversion is done, use `download_image` to fetch the resulting file data. You can then pass this live response into subsequent code blocks for saving or further API calls. It ensures your generated code handles real-world API responses, not just mock placeholders.

Clean Up Image Tokens in Code

After you're done with an image token, use `delete_image`. This tool manually removes the stored bitmap file that the server used for processing. It's vital for cleaning up your project scope and managing resource tokens properly.

Setup guide

Set up Vectorizer AI MCP in Cursor

Prerequisites

Cursor installed (macOS, Windows, or Linux)
Active Vinkius subscription with a valid endpoint token

1

Open MCP Settings

Go to Cursor Settings → MCP or open the Command Palette (Cmd+Shift+P / Ctrl+Shift+P) and search for "MCP: Add Server".
2

Add the Vectorizer AI MCP

Cursor will create or open .cursor/mcp.json in your project root. Paste the JSON snippet on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Enable Agent mode

Open Composer (Cmd+I / Ctrl+I) and switch to Agent mode using the dropdown at the top. MCP tools are only available in Agent mode.
4

Verify the connection

Ask Cursor something like "List my recent Vectorizer AI transactions." If the MCP tools are loaded correctly, Cursor will call the Vectorizer AI tools automatically. You can also check Settings → MCP for a green status indicator.

.cursor/mcp.json

{
  "mcpServers": {
    "vectorizer-ai-mcp": {
      "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 Cursor

You add the server to your `.cursor/mcp.json` file, then enable Agent mode in chat. The agent can then call `vectorize_image` as if it were a native function.

Yes, use `get_account_status`. This tool checks your subscription status and remaining usage limits before you run code that requires heavy vectorization processing.

It needs a bitmap image token (like JPG or PNG) to feed into the `vectorize_image` tool. The output is always high-quality vectors like SVG, EPS, or PDF.

You must define the server configuration in a local `.cursor/mcp.json` file and ensure Agent mode is active for the tools to be accessible during coding.

The `delete_image` tool removes the specific bitmap image token. This cleans up your project scope and prevents unnecessary resource usage.

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