How to Use the Conductor (Netflix OSS) MCP in Vercel AI SDK

Stream Netflix Conductor workflow data directly into your Vercel AI SDK interface for live user-facing updates.

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Connect Conductor (Netflix OSS) MCP to Vercel AI SDK

Create your Vinkius account to connect Conductor (Netflix OSS) 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 Conductor (Netflix OSS) with Vercel AI SDK

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Real-time workflow monitoring

Pipe execution states straight into your React components. Your users watch tasks progress without waiting for a page refresh. Use `get_workflow` or `get_running_workflows` to pull status updates. The AI SDK handles the stream so your UI reflects the backend state instantly.

Execution control from the browser

Trigger processes directly from your frontend application logic. You can fire off new work or pause active jobs with a single click. Call `start_workflow` or `pause_workflow` to bridge your interface with the backend. This gives your users direct control over their production pipelines.

Task lifecycle management

Adjust task definitions or update results while the app is live. This MCP server lets your frontend react to changes in your orchestration logic. Update status with `update_task` or tweak definitions using `update_task_definition`. You keep your application logic synced with the orchestrator.

Setup guide

Set up Conductor (Netflix OSS) 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 Conductor (Netflix OSS) 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 Conductor (Netflix OSS) 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 Conductor (Netflix OSS). 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 Conductor (Netflix OSS) MCP in Vercel AI SDK

Install the MCP client packages and point your transport to the Vinkius endpoint. You then pass the server tools into your streamText configuration to start executing commands.

Yes, you can stream task logs directly into your UI. Use the tool outputs to update your frontend state as the workflow progresses.

It works perfectly within Edge Functions or Node.js runtimes. You just initialize the client and call the tools as part of your request handler.

Not at all. You provide the endpoint token and map the tools to your agents. Everything else runs via standard HTTP transports.

We keep your workflow definitions and execution logs encrypted in transit. Your AI client only receives the specific task data you request during a session.

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