How to Use the Vectorizer AI MCP in Vercel AI SDK

Q: How does Vectorizer AI handle image conversion with Vercel AI SDK?

You call vectorizeimage and pass it the bitmap data. The tool handles tracing that JPG or PNG into scalable vector files (SVG, EPS, etc.). You'll get the resulting file data back in your stream.

Q: Can I check resource limits for Vectorizer AI using Vercel AI SDK?

Yes. Just use getaccountstatus. This tool immediately tells you the user's subscription status and how many credits they have left before attempting any vectorization.

Q: How do I clean up old images from Vectorizer AI using Vercel AI SDK?

You use the deleteimage tool. It lets you manually delete an image that was stored previously, especially if its retention days have passed.

Stream live vectorization results directly into your React or Next.js UI using Vercel AI SDK.

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Connect Vectorizer AI MCP to Vercel AI SDK

Create your Vinkius account to connect Vectorizer AI to Vercel AI SDK 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 Vercel AI SDK

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Vectorize bitmap graphics in real time

The `vectorize_image` tool converts raster images (like JPG and PNG) into clean, scalable vector formats. Your agent sends the input image, and you get back a high-fidelity SVG, EPS, PDF, or DXF file ready for display. You can stream this process directly to the user's interface. This means the client sees the data appearing instantly—no loading spinner needed.

Check account limits and credits

Need to know if the job will actually run? Use `get_account_status` to pull the user's current subscription standing and remaining credits. This check is crucial before starting any resource-intensive task. It keeps your frontend logic clean by validating capacity upfront, preventing failed operations that just waste time.

Manage uploaded image assets

The `delete_image` tool lets you programmatically remove images stored under a retention policy. This is essential for cleanup and managing user data lifecycle. You can also use `download_image` to grab the final, processed vector file or any additional formats needed for your frontend display.

Setup guide

Set up Vectorizer AI MCP in Vercel AI SDK

Prerequisites

Node.js 18+ and a TypeScript project
ai + @modelcontextprotocol/sdk packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies

Run npm install ai @modelcontextprotocol/sdk plus your preferred model provider (e.g. @ai-sdk/openai).
2

Create the Streamable HTTP transport

Use StreamableHTTPClientTransport with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and use tools

Call mcpClient.tools() to auto-discover all Vectorizer AI tools. Pass them directly to generateText() or streamText() — no manual schema definitions needed.
4

Works with any model provider

Swap openai("gpt-4o") for any AI SDK provider — Anthropic, Google, Mistral. The MCP tools work identically across all supported models.

index.ts

import { experimental_createMCPClient as createMCPClient } from "ai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const transport = new StreamableHTTPClientTransport(
  new URL("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
);

const mcpClient = await createMCPClient({ transport });
const tools = await mcpClient.tools();

const { text } = await generateText({
  model: openai("gpt-4o"),
  tools,
  prompt: "List recent Vectorizer AI transactions",
});

console.log(text);
await mcpClient.close();

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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 Vercel AI SDK

You call `vectorize_image` and pass it the bitmap data. The tool handles tracing that JPG or PNG into scalable vector files (SVG, EPS, etc.). You'll get the resulting file data back in your stream.

Yes. Just use `get_account_status`. This tool immediately tells you the user's subscription status and how many credits they have left before attempting any vectorization.

This server touches the raw PNG or JPG data inputs, as well as the resulting temporary image tokens. You'll need to handle these tokens carefully for cleanup.

Because you can stream the results, yes. The user sees data appearing in real-time as it processes through the `vectorize_image` tool.

You use the `delete_image` tool. It lets you manually delete an image that was stored previously, especially if its retention days have passed.

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