How to Use the Flatwork ATS MCP in Vercel AI SDK

Q: How do I handle candidate data streaming in Vercel AI SDK with Flatwork ATS?

You pass the tools from createMCPClient directly to streamText. When the agent calls listapplicants, the SDK streams the candidate arrays straight to your frontend components. Remember to call mcpClient.close() when the connection finishes.

Q: What is the setup process for the Flatwork ATS MCP Server with Vercel AI SDK?

Install @ai-sdk/mcp and configure the client with your Vinkius HTTP endpoint. Pass the tools from mcpClient.tools() into generateText so your agent can fetch job details using getjob.

Q: How does the agent handle webhook monitoring?

The agent runs listwebhooks to inspect active event listeners. The Vercel AI SDK then displays these active endpoints in your admin dashboard in real-time.

Run real-time candidate pipelines directly in your React apps with Flatwork ATS and the Vercel AI SDK MCP client.

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Connect Flatwork ATS MCP to Vercel AI SDK

Create your Vinkius account to connect Flatwork ATS 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 Flatwork ATS with Vercel AI SDK

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Push applicant status changes directly to your UI

Look, here's the deal. The `update_application_status` tool lets your Vercel AI SDK agent change hiring stages on the fly. Because the SDK handles streaming, your users see the status flip in the UI without waiting for a loading spinner. You can couple this with `list_applications` to render live hiring queues. The agent handles the data mutations, while the Vercel AI SDK pushes the fresh state directly to your React components.

Create candidates instantly via Vercel AI SDK

When a user drops a resume into your chat interface, your agent uses `create_applicant` to parse and write the record to Flatwork ATS. The SDK passes the JSON payload straight to the Edge Function, keeping execution fast. Once created, the agent calls `get_applicant` to pull the new profile. The SDK streams the parsed details right back to the client interface so the recruiter sees the profile build itself.

Live job board queries using this MCP Server

Connect your web portal to Flatwork ATS via this MCP Server to let users query open roles. Your agent runs `list_jobs` and `get_job` to fetch active postings based on user questions. The Vercel AI SDK streams these job descriptions directly into custom UI cards. This bypasses the typical loading spinners, showing users matched roles the millisecond the agent finds them.

Setup guide

Set up Flatwork ATS 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 Flatwork ATS 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 Flatwork ATS 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 Flatwork ATS. 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 Flatwork ATS MCP in Vercel AI SDK

You pass the tools from `createMCPClient` directly to `streamText`. When the agent calls `list_applicants`, the SDK streams the candidate arrays straight to your frontend components. Remember to call `mcpClient.close()` when the connection finishes.

Yes, this MCP Server works in Edge environments. The agent runs `update_application_status` within your Edge route, avoiding cold starts and updating candidate stages instantly.

Install `@ai-sdk/mcp` and configure the client with your Vinkius HTTP endpoint. Pass the tools from `mcpClient.tools()` into `generateText` so your agent can fetch job details using `get_job`.

The agent runs `list_webhooks` to inspect active event listeners. The Vercel AI SDK then displays these active endpoints in your admin dashboard in real-time.

Vinkius runs this MCP Server in an isolated V8 sandbox, meaning candidate emails, resumes, and hiring stages are never stored on our servers. Your API keys are encrypted, and data flows directly between Flatwork ATS and your Vercel deployment.

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