How to Use the Arize AI MCP in Vercel AI SDK

Q: Can Vercel AI SDK display Arize AI model drift data?

Yes, you call getmetrics through the MCP connection and stream the resulting JSON directly into your UI components. Your frontend renders the drift analysis as the data arrives from the server.

Stream Arize AI observability data directly into your Vercel AI SDK frontends for real-time model monitoring.

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Connect Arize AI MCP to Vercel AI SDK

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

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Live model performance in Vercel AI SDK

Feed your UI live observability metrics by calling `get_metrics` within your streaming response handlers. Your React or Next.js components update instantly as the model telemetry arrives. Stop waiting for background jobs to finish. Use `get_model` to pull schema definitions and bind them to your frontend inputs immediately.

Automated evaluation workflows

Trigger specific model assessments using `run_eval` directly from your edge functions. You can verify outputs against your existing Arize AI evaluation configurations without leaving your codebase. Check your environment status with `list_environments` to ensure your production logs land in the correct workspace. It keeps your dev and prod data strictly separated.

Telemetry logging for Vercel AI SDK

Push raw interaction logs to your platform using `ingest_log` inside your server-side actions. This keeps a tight feedback loop between your live application and your monitoring dashboard. Manage your evaluation datasets by calling `list_datasets` and `get_dataset`. You control exactly which historical data feeds into your current Vercel AI SDK logic.

Setup guide

Set up Arize 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 Arize 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 Arize 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 Arize 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.

Why Choose Vinkius

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Real-time monitoring

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Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

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lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

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place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Arize AI MCP in Vercel AI SDK

Install the MCP client packages and point your transport URL to the Vinkius endpoint. You then pass the tools from `mcpClient.tools()` into your `streamText` configuration to make them available to your agent.

Yes, you call `get_metrics` through the MCP connection and stream the resulting JSON directly into your UI components. Your frontend renders the drift analysis as the data arrives from the server.

The MCP server is fully compatible with edge runtimes. You keep your latency low while pulling model metadata or triggering evaluation runs during the request lifecycle.

Vinkius manages the authentication layer, so you only handle the endpoint token. Your API keys never touch the client-side code, keeping your telemetry ingestion secure.

Your telemetry logs containing inference payloads and model features stay encrypted in transit. Vinkius operates a zero-trust sandbox, ensuring that only your authorized Vercel AI SDK deployment interacts with your specific model workspace.

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