How to Use the Cloudinary MCP in Vercel AI SDK

Q: How do I stream Cloudinary asset searches with the Vercel AI SDK?

You call searchmedialibrary inside your streamText function. The Vercel AI SDK handles the tool call, fetches the matching assets from your Cloudinary account, and streams the URLs directly to your React frontend.

Q: Can I use this Cloudinary integration with Vercel AI SDK on Edge Functions?

Yes. You initialize the client using createMCPClient inside your edge route. Because the MCP Server runs in a secure, isolated V8 sandbox on Vinkius, your edge functions stay lightweight and fast.

Q: Can my Vercel AI SDK agent delete Cloudinary assets using this toolset?

Yes, the agent can call deletemediaresource if you authorize it. This lets you build UI components where users can delete Cloudinary files directly through a chat interface.

Feed live Cloudinary media metrics and asset details directly into your Next.js UI with Vercel AI SDK streaming.

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Connect Cloudinary MCP to Vercel AI SDK

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

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Live Media Asset Search

`search_media_library` lets your Next.js app query your Cloudinary assets instantly. When a user types a search term, your Vercel AI SDK agent executes the query and streams the matching image or video metadata directly into the React component. You don't have to wait for a massive Cloudinary API payload to resolve before rendering your React page. By wrapping `search_media_library` in `streamText`, the matching asset URLs render on the fly, creating a responsive UI that feels instant.

Real-time Usage Dashboards with Vercel AI SDK

This MCP Server exposes `get_cloudinary_usage_report` to let your React frontend show live storage and bandwidth metrics. Your Vercel AI SDK client calls this tool to check how much of your Cloudinary quota is left this month. Instead of building a complex backend polling mechanism, you let the agent fetch the Cloudinary metrics on edge functions. The numbers stream straight to your custom Vercel AI SDK dashboard component, letting users see their exact bandwidth consumption without a single page reload.

On-the-Fly Asset Management

`list_media_transformations` and `list_upload_presets` let your frontend agent inspect how your Cloudinary account handles incoming media. Your Vercel AI SDK setup can instantly display these configurations to users who need to apply specific image crops or video formats. By integrating these MCP tools directly into `generateText` calls, your UI adapts dynamically based on what presets are actually available in your Cloudinary account. This keeps your Vercel AI SDK frontend in sync with your media library configuration without hardcoding a single endpoint.

Setup guide

Set up Cloudinary 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 Cloudinary 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 Cloudinary 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 Cloudinary. 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 Cloudinary MCP in Vercel AI SDK

You call `search_media_library` inside your `streamText` function. The Vercel AI SDK handles the tool call, fetches the matching assets from your Cloudinary account, and streams the URLs directly to your React frontend.

Yes. You initialize the client using `createMCPClient` inside your edge route. Because the MCP Server runs in a secure, isolated V8 sandbox on Vinkius, your edge functions stay lightweight and fast.

You don't expose your Cloudinary API secrets to the frontend. Vinkius manages the connection securely on the server side, and the Vercel AI SDK communicates via a single secure endpoint token.

Yes, the agent can call `delete_media_resource` if you authorize it. This lets you build UI components where users can delete Cloudinary files directly through a chat interface.

The Cloudinary MCP Server only touches your Cloudinary media metadata, usage metrics, and asset URLs. Vinkius processes these requests in a zero-trust, ephemeral sandbox, ensuring your private asset paths and API keys are never stored or exposed to external networks.

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