How to Use the Workload MCP in Vercel AI SDK

Q: How do I check a Workload status using Vercel AI SDK?

Call checkworkloadstatus and stream the result. Since your application is built with the Vercel AI SDK, the user sees this connection verification happening live on their screen.

Q: Can I list all Workload automations in my UI using Vercel AI SDK?

Yeah, use listworkflows. This fetches all existing workflow definitions. You can then stream that list to your frontend, allowing the user to see a complete overview of what's automated.

Q: If an execution fails, how do I handle retries with Vercel AI SDK?

You use retryexecution. Because you’re using the Vercel AI SDK, when the retry is initiated and successful, the status update streams back in real time to the user's browser.

Q: How do I get detailed execution results using Vercel AI SDK?

You call getexecution to pull the specific results you need. Streaming these details into your UI makes the whole experience feel instantaneous and highly responsive for the end user.

Stream live workflow results directly into your frontend with Vercel AI SDK.

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Connect Workload MCP to Vercel AI SDK

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

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Live Status Updates with the MCP Server

Need to show the user if a background job is running? Use `check_workload_status` to verify connectivity and tell your client exactly where things stand. This status check streams in real-time, so you don't have to make them wait for a loading spinner. You can also use `list_executions` or `get_execution` to pull live details on running jobs. Because the Vercel AI SDK handles streaming data directly into React and Next.js, users see this status appear as if it were generated by the AI itself.

Creating and Managing Workflows in the Client

Building a new automated process is simple using `create_workflow`. Your agent can call this tool, and you stream back confirmation that the workflow was set up. If things go sideways later, developers can use `list_workflows` to show users an overview of all existing automation pipelines. Furthermore, if a flow needs manual control, your AI client handles calling `enable_workflow` or `disable_workflow`. This lets you expose full lifecycle management to the end user's interface.

Debugging and Monitoring MCP Server Runs

When troubleshooting for a user, logging is everything. Use `list_logs` to fetch workflow logs directly into your component. This lets you show step-by-step debugging output as it appears. If an execution fails, don't worry. You can use `retry_execution`. The result streams back immediately, showing the user that the job has been kicked off again—a much better experience than just saying 'job retried'.

Setup guide

Set up Workload 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 Workload 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 Workload 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 Workload. 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 Workload MCP in Vercel AI SDK

Call `check_workload_status` and stream the result. Since your application is built with the Vercel AI SDK, the user sees this connection verification happening live on their screen.

Yeah, use `list_workflows`. This fetches all existing workflow definitions. You can then stream that list to your frontend, allowing the user to see a complete overview of what's automated.

You use `retry_execution`. Because you’re using the Vercel AI SDK, when the retry is initiated and successful, the status update streams back in real time to the user's browser.

This server manages workflow definitions, execution details, connection credentials, and operational logs. This means it handles internal business process data.

You call `get_execution` to pull the specific results you need. Streaming these details into your UI makes the whole experience feel instantaneous and highly responsive for the end user.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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