How to Use the NVIDIA API Catalog MCP in Vercel AI SDK

Q: How do I monitor NVIDIA API Catalog usage in Vercel AI SDK?

Call nvidiachecktokenquota before your streamText function runs. This returns your current credit balance and constraint limits. You can then gate your UI actions based on these values.

Q: Can I use NVIDIA API Catalog vision tools with Vercel AI SDK?

Yes. Pass your image buffers to nvidiavisioninference and pipe the result into your frontend state. It handles the multimodal payload natively through the MCP connection.

Q: How do I list available models for my Vercel AI SDK project?

Run nvidialistfoundationmodels to get the current array of active LLMs. This helps you dynamically populate dropdowns or select the right model for your specific task.

Stream Nemotron and Llama3 completions directly into your Vercel AI SDK frontends for live, low-latency UI updates.

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Connect NVIDIA API Catalog MCP to Vercel AI SDK

Create your Vinkius account to connect NVIDIA API Catalog 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 NVIDIA API Catalog with Vercel AI SDK

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Real-time AI data streaming

Pipe inference results directly into your React or Next.js components without waiting for backend bottlenecks. You use `nvidia_chat_completion` to push model outputs straight to the user's view while the connection stays open. This keeps your interface snappy. By handling the stream in your Edge Functions, you remove the latency lag that usually kills performance in production apps.

Visual inference for your UI

Send image data to your AI client to get instant descriptive feedback in the browser. You trigger `nvidia_vision_inference` to analyze graphical inputs and update the DOM immediately. It works by passing the image payload to the server and rendering the response as it arrives. Your users see the analysis happen in front of their eyes instead of staring at a loading spinner.

Monitor your model budget

Keep tabs on your token consumption within the Vercel AI SDK lifecycle to prevent surprise overages. You call `nvidia_check_token_quota` before initiating large generation tasks. This gives you programmatic control over your costs. If your quota hits a ceiling, you can switch models or halt requests before your bill spikes unexpectedly.

Setup guide

Set up NVIDIA API Catalog 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 NVIDIA API Catalog 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 NVIDIA API Catalog transactions",
});

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

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Common questions about NVIDIA API Catalog MCP in Vercel AI SDK

Call `nvidia_check_token_quota` before your `streamText` function runs. This returns your current credit balance and constraint limits. You can then gate your UI actions based on these values.

Yes. Pass your image buffers to `nvidia_vision_inference` and pipe the result into your frontend state. It handles the multimodal payload natively through the MCP connection.

It is. Since the server runs on standard HTTP transports, your edge code can reach the API endpoints without issues. Just ensure your environment variables are configured correctly.

Run `nvidia_list_foundation_models` to get the current array of active LLMs. This helps you dynamically populate dropdowns or select the right model for your specific task.

It does. You can chain multiple tool calls and stream their individual outputs. This is useful for summarization tasks where you want to show progress to the end user.

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