Arize AI MCP Server for Vercel AI SDKGive Vercel AI SDK instant access to 6 tools to Create Dataset, Get Model, List Datasets, and more
The Vercel AI SDK is the TypeScript toolkit for building AI-powered applications. Connect Arize AI through Vinkius and every tool is available as a typed function. ready for React Server Components, API routes, or any Node.js backend.
Ask AI about this App Connector for Vercel AI SDK
The Arize AI app connector for Vercel AI SDK is a standout in the Friends Mcp category — giving your AI agent 6 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
import { createMCPClient } from "@ai-sdk/mcp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";
async function main() {
const mcpClient = await createMCPClient({
transport: {
type: "http",
// Your Vinkius token. get it at cloud.vinkius.com
url: "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
},
});
try {
const tools = await mcpClient.tools();
const { text } = await generateText({
model: openai("gpt-4o"),
tools,
prompt: "Using Arize AI, list all available capabilities.",
});
console.log(text);
} finally {
await mcpClient.close();
}
}
main();
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Arize AI MCP Server
Connect your Arize AI account to any AI agent and take full control of your machine learning observability and automated model monitoring workflows through natural conversation.
The Vercel AI SDK gives every Arize AI tool full TypeScript type inference, IDE autocomplete, and compile-time error checking. Connect 6 tools through Vinkius and stream results progressively to React, Svelte, or Vue components. works on Edge Functions, Cloudflare Workers, and any Node.js runtime.
What you can do
- Project & Trace Orchestration — List and monitor active ML tracing projects programmatically, retrieving detailed high-fidelity execution spans and telemetry data in real-time
- Dataset Lifecycle Management — Programmatically create and manage datasets for model evaluation and validation to maintain a perfectly coordinated ML infrastructure
- Experiment Monitoring — Access and track ML experiments to understand high-fidelity model performance, drift, and data quality across different environments
- Model Intelligence Discovery — Retrieve detailed metadata for specific ML models to coordinate your organizational AI strategy directly through your agent
- Operational Monitoring — Access account-level settings and verify API connectivity directly through your agent for instant performance reporting
The Arize AI MCP Server exposes 6 tools through the Vinkius. Connect it to Vercel AI SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 6 Arize AI tools available for Vercel AI SDK
When Vercel AI SDK connects to Arize AI through Vinkius, your AI agent gets direct access to every tool listed below — spanning ml-observability, model-monitoring, data-drift, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Create a dataset
Get model details
List datasets
List experiments
List projects
List spans
Connect Arize AI to Vercel AI SDK via MCP
Follow these steps to wire Arize AI into Vercel AI SDK. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
npm install @ai-sdk/mcp ai @ai-sdk/openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the script
agent.ts and run with npx tsx agent.tsExplore tools
Why Use Vercel AI SDK with the Arize AI MCP Server
Vercel AI SDK provides unique advantages when paired with Arize AI through the Model Context Protocol.
TypeScript-first: every MCP tool gets full type inference, IDE autocomplete, and compile-time error checking out of the box
Framework-agnostic core works with Next.js, Nuxt, SvelteKit, or any Node.js runtime. same Arize AI integration everywhere
Built-in streaming UI primitives let you display Arize AI tool results progressively in React, Svelte, or Vue components
Edge-compatible: the AI SDK runs on Vercel Edge Functions, Cloudflare Workers, and other edge runtimes for minimal latency
Arize AI + Vercel AI SDK Use Cases
Practical scenarios where Vercel AI SDK combined with the Arize AI MCP Server delivers measurable value.
AI-powered web apps: build dashboards that query Arize AI in real-time and stream results to the UI with zero loading states
API backends: create serverless endpoints that orchestrate Arize AI tools and return structured JSON responses to any frontend
Chatbots with tool use: embed Arize AI capabilities into conversational interfaces with streaming responses and tool call visibility
Internal tools: build admin panels where team members interact with Arize AI through natural language queries
Example Prompts for Arize AI in Vercel AI SDK
Ready-to-use prompts you can give your Vercel AI SDK agent to start working with Arize AI immediately.
"List all active ML projects in my Arize account."
"Show the recent execution spans for project '1024'."
"Create a new dataset 'Q2_Eval_Data' for model evaluation."
Troubleshooting Arize AI MCP Server with Vercel AI SDK
Common issues when connecting Arize AI to Vercel AI SDK through the Vinkius, and how to resolve them.
createMCPClient is not a function
npm install @ai-sdk/mcpArize AI + Vercel AI SDK FAQ
Common questions about integrating Arize AI MCP Server with Vercel AI SDK.
How does the Vercel AI SDK connect to MCP servers?
createMCPClient from @ai-sdk/mcp and pass the server URL. The SDK discovers all tools and provides typed TypeScript interfaces for each one.Can I use MCP tools in Edge Functions?
Does it support streaming tool results?
useChat and streamText that handle tool calls and display results progressively in the UI.