How to Use the Relay Workflow Automation MCP in Vercel AI SDK

Q: Does Relay Workflow Automation support real-time status updates in Vercel AI SDK?

It does. You can poll getrunstatus or use the streaming nature of your SDK to pipe results back to the interface as the workflow progresses.

Q: How do I cancel a running job using Vercel AI SDK?

Call the cancelrun tool with the run ID. It stops the execution immediately if the process is still active.

Trigger Relay automations directly from your Vercel AI SDK frontends to update your UI with real-time workflow data.

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Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Relay Workflow Automation MCP to Vercel AI SDK

Create your Vinkius account to connect Relay Workflow Automation to Vercel AI SDK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

GDPR Included with Plan

Setup Relay Workflow Automation with Vercel AI SDK

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Real-time workflow execution in Vercel AI SDK

Trigger your backend automations using `run_workflow` while streaming results straight to your React or Next.js components. You get the output in your UI as it happens instead of waiting for a full process to finish. Your users see the state change immediately. This MCP Server keeps your frontend in sync with your background tasks.

Manage Relay runs from your edge functions

Check the status of any task with `get_run_status` to show progress bars or loading states in your app. You can also use `list_runs` to pull history for user dashboards. Everything happens over the Vercel AI SDK transport layer. It’s built for low-latency updates where the end user needs to know exactly what the system is doing.

Discover available Relay automations

Use `list_workflows` to fetch your account’s active processes and map them to UI buttons or actions. The `get_workflow` tool provides the specific schema you need to build your input forms. Stop hardcoding your automation IDs. Let your AI client fetch the latest configuration from Relay at runtime.

Setup guide

Set up Relay Workflow Automation 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 Relay Workflow Automation 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 Relay Workflow Automation 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 Relay. 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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Common questions about Relay Workflow Automation MCP in Vercel AI SDK

Install the MCP client package and initialize it with your endpoint. Pass the tools from this MCP Server into your `streamText` function to start executing workflows.

Yes. You pass your JSON input variables directly through the `run_workflow` tool. The client handles the request, and the server processes the payload.

It does. You can poll `get_run_status` or use the streaming nature of your SDK to pipe results back to the interface as the workflow progresses.

Call the `cancel_run` tool with the run ID. It stops the execution immediately if the process is still active.

Your API token stays on your backend or edge function. Only the specific workflow execution data you explicitly trigger is sent to the Relay API.

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