How to Use the Comet ML MCP in Vercel AI SDK

Stream Comet ML data straight into your Vercel AI SDK interface so users see live experiment metrics as they happen.

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Connect Comet ML MCP to Vercel AI SDK

Create your Vinkius account to connect Comet ML 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 Comet ML with Vercel AI SDK

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Live experiment tracking in Vercel AI SDK

Pipe `get_experiment` results directly into your React components. Your UI renders the payload IDs as the agent fetches them from the server. This MCP Server pushes data through the SDK transport layer. You avoid the lag of standard request-response cycles.

Component-ready metric streams

Feed `get_experiment_metrics` output into your charts without manual parsing. The numeric bounds arrive ready for your visualization libraries. Your front-end reacts to the data stream instantly. Users track model performance updates without refreshing the page.

Workspace auditing on the edge

Query `list_workspaces` to show project structures in your dashboard. You map the routing spaces directly to your navigation menus. It runs on Edge Functions just fine. You keep your app footprint small while maintaining full access to Comet ML.

Setup guide

Set up Comet ML 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 Comet ML 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 Comet ML 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 Comet ML. 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

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Comet ML now

Real-time monitoring

Live

visibility into every interaction

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

Built-in savings

60%

lower AI costs

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

Single dashboard

One

place for every integration

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

Common questions about Comet ML MCP in Vercel AI SDK

You instantiate the client using the provided transport URL. Pass the tool definitions into your generateText call to let the agent handle the fetching.

Yes. Use the listing tools to narrow down specific projects before sending the data to your UI.

It does. You get the tool output as a stream which your components consume in real-time.

You provide an endpoint token via the authProvider configuration. This keeps your credentials out of the client-side code.

We enforce strict isolation between your server and the agent. The system only processes your experiment parameters and numeric logs through encrypted transport channels.

Use it with your favorite AI tools

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