How to Use the Claid AI MCP in Vercel AI SDK

Q: How do I connect the Claid AI MCP Server to my Vercel AI SDK project?

Install @ai-sdk/mcp and call createMCPClient pointing to your Vinkius endpoint. Pass the tools directly into generateText or streamText to let your agent call the image tools. Remember to call close() when the session ends.

Q: How does Vercel AI SDK handle long-running Claid AI upscaling tasks?

The agent uses getprocessingtaskdetails to check the status of async image tasks. This lets your Vercel AI SDK UI poll for the final high-resolution image URL while keeping the user informed of the progress.

Q: How does the Claid AI integration with Vercel AI SDK handle image data privacy?

All image URLs, metadata, and raw image files processed by enhanceimage are handled in a secure, ephemeral V8 sandbox. Your source image URLs are never stored permanently, and session data is wiped immediately after the operation completes.

Feed processed product images directly into your Next.js UI in real-time with the Vercel AI SDK and Claid AI.

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Connect Claid AI MCP to Vercel AI SDK

Create your Vinkius account to connect Claid 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 Claid AI with Vercel AI SDK

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Live photo upscaling with Vercel AI SDK

The `upscale_image_resolution` tool increases image resolution using AI models, sending the high-res asset back to your application. By passing this tool to your Vercel AI SDK `streamText` function, your UI shows the progress of the upscaling task without making the user wait for a full page reload. Your Next.js frontend renders the resulting image URL the millisecond the background task finishes. You avoid loading spinners because the SDK handles the asynchronous state updates directly in the React component tree.

Instant background removal in Next.js

The `remove_image_background` tool strips or replaces image backgrounds to prepare raw product shots for your storefront. Your Vercel AI SDK agent triggers this action when a merchant uploads a new file, executing the edit instantly at the edge. This setup means your web app handles heavy image manipulation without server-side bottlenecks. It's built so the edge-compatible client processes the request and updates the UI state immediately.

Multi-step edits via Vercel AI SDK MCP Server

The `enhance_image` tool merges upscaling, background removal, and HDR adjustments into a single operation for rapid product catalog updates. Your AI client calls this tool through the Vercel AI SDK to fix poor lighting and low resolution in one pass. It matches the native React rendering cycle perfectly. The agent receives the updated collection data and renders the polished product card before the user even thinks about refreshing.

Setup guide

Set up Claid 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 Claid 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 Claid AI transactions",
});

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

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 Claid 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 Claid AI MCP in Vercel AI SDK

Install `@ai-sdk/mcp` and call `createMCPClient` pointing to your Vinkius endpoint. Pass the tools directly into `generateText` or `streamText` to let your agent call the image tools. Remember to call `close()` when the session ends.

Yes, the Vercel AI SDK integration is fully compatible with Edge runtimes. Your agent can call `remove_image_background` or `enhance_image` from a lightweight edge route without hitting server timeouts.

The agent uses `get_processing_task_details` to check the status of async image tasks. This lets your Vercel AI SDK UI poll for the final high-resolution image URL while keeping the user informed of the progress.

No, Vinkius handles the authentication for you. Your Vercel AI SDK code only needs one MCP server endpoint token to access all eight image processing tools safely.

All image URLs, metadata, and raw image files processed by `enhance_image` are handled in a secure, ephemeral V8 sandbox. Your source image URLs are never stored permanently, and session data is wiped immediately after the operation completes.

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