How to Use the Clarifai (Vision AI) MCP in Vercel AI SDK

Q: How do I connect Clarifai (Vision AI) to the Vercel AI SDK?

Install @ai-sdk/mcp. Use createMCPClient with your Vinkius endpoint URL, call mcpClient.tools(), and pass those into streamText.

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Connect Clarifai (Vision AI) MCP to Vercel AI SDK

Create your Vinkius account to connect Clarifai (Vision 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 Clarifai (Vision AI) with Vercel AI SDK

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Claude

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Stream Clarifai Predictions via MCP Server

`predict_model` executes computer vision tasks and sends the results straight to your frontend. Your Vercel AI SDK setup streams these bounding boxes and classification tags directly into React components while the user watches. Forget polling a backend API. The UI updates instantly. You pass the image URL, the agent hits the model, and the visual data renders live in the browser.

Map Custom Vision Taxonomies

`list_concepts` pulls the exact semantic tags attached to your visual datasets. Your generative UI builds dynamic filtering menus based on what the Clarifai app actually knows how to detect. Combine this with `list_models` to let users switch between different computer vision parameters on the fly. The agent reads the available models and updates your Svelte or Vue interface without hardcoding a single ID.

Expose Chained Inference Workflows

`list_workflows` retrieves the structural matching for chained AI limits. If your application needs to run a face detector followed by an age classifier, this tool finds that exact sequence. `list_apps` manages the global compute limits across these operations. Your agent checks the bounded apps before initiating heavy inference, keeping your edge functions running within acceptable timeouts.

Setup guide

Set up Clarifai (Vision 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 Clarifai (Vision 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 Clarifai (Vision 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 Clarifai. 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 Clarifai (Vision AI) MCP in Vercel AI SDK

Install `@ai-sdk/mcp`. Use `createMCPClient` with your Vinkius endpoint URL, call `mcpClient.tools()`, and pass those into `streamText`.

Yes. The agent calls the prediction tool and streams the resulting JSON tags back to your generative UI components in real-time.

No. The agent uses the listing tools to find the correct model dynamically before running an inference task.

You retrieve the configured sequences using the workflow listing tool. The agent then executes the entire chain in one call rather than managing multiple round-trips from the edge function.

Image URLs and classification tags route through a V8 Isolate Sandbox on Vinkius. The ephemeral environment destroys itself immediately after the prediction returns, leaving zero trace of your visual data.

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