Flatwork ATS MCP Server for Vercel AI SDKGive Vercel AI SDK instant access to 8 tools to Create Applicant, Get Applicant, Get Job, and more
GDPR Free for SubscribersThe Vercel AI SDK is the TypeScript toolkit for building AI-powered applications. Connect Flatwork ATS through Vinkius and every tool is available as a typed function. ready for React Server Components, API routes, or any Node.js backend.
Ask AI about this MCP Server for Vercel AI SDK
The Flatwork ATS MCP Server for Vercel AI SDK is a standout in the Productivity category — giving your AI agent 8 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
import { createMCPClient } from "@ai-sdk/mcp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";
async function main() {
const mcpClient = await createMCPClient({
transport: {
type: "http",
// Your Vinkius token. get it at cloud.vinkius.com
url: "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
},
});
try {
const tools = await mcpClient.tools();
const { text } = await generateText({
model: openai("gpt-4o"),
tools,
prompt: "Using Flatwork ATS, list all available capabilities.",
});
console.log(text);
} finally {
await mcpClient.close();
}
}
main();
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Flatwork ATS MCP Server
Connect your Flatwork ATS account to any AI agent and take full control of your recruitment pipeline and candidate management workflows through natural conversation.
The Vercel AI SDK gives every Flatwork ATS tool full TypeScript type inference, IDE autocomplete, and compile-time error checking. Connect 8 tools through Vinkius and stream results progressively to React, Svelte, or Vue components. works on Edge Functions, Cloudflare Workers, and any Node.js runtime.
What you can do
- Job Orchestration — List all open and closed job postings and retrieve detailed metadata, including hiring teams and job requirements programmatically
- Candidate Tracking — Manage your complete directory of applicants and retrieve detailed profiles and contact information programmatically
- Application Lifecycle — Monitor active job applications and update candidate hiring stages (Interview, Hired, Rejected) directly through your agent
- Applicant Discovery — Programmatically create new candidates in the system using external data to automate your sourcing pipeline
- System Monitoring — List configured webhooks to understand real-time data flows and ensure high-fidelity synchronization with your HR tools
The Flatwork ATS MCP Server exposes 8 tools through the Vinkius. Connect it to Vercel AI SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 8 Flatwork ATS tools available for Vercel AI SDK
When Vercel AI SDK connects to Flatwork ATS through Vinkius, your AI agent gets direct access to every tool listed below — spanning hiring-pipeline, candidate-tracking, job-postings, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.
Pass applicant data as a JSON string. Add a new candidate
Get applicant details
Get job details
List all applicants/candidates
List all job applications
List all job postings
List configured webhooks
Update application hiring stage
Connect Flatwork ATS to Vercel AI SDK via MCP
Follow these steps to wire Flatwork ATS into Vercel AI SDK. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.
Install dependencies
npm install @ai-sdk/mcp ai @ai-sdk/openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the script
agent.ts and run with npx tsx agent.tsExplore tools
Why Use Vercel AI SDK with the Flatwork ATS MCP Server
Vercel AI SDK provides unique advantages when paired with Flatwork ATS through the Model Context Protocol.
TypeScript-first: every MCP tool gets full type inference, IDE autocomplete, and compile-time error checking out of the box
Framework-agnostic core works with Next.js, Nuxt, SvelteKit, or any Node.js runtime. same Flatwork ATS integration everywhere
Built-in streaming UI primitives let you display Flatwork ATS tool results progressively in React, Svelte, or Vue components
Edge-compatible: the AI SDK runs on Vercel Edge Functions, Cloudflare Workers, and other edge runtimes for minimal latency
Flatwork ATS + Vercel AI SDK Use Cases
Practical scenarios where Vercel AI SDK combined with the Flatwork ATS MCP Server delivers measurable value.
AI-powered web apps: build dashboards that query Flatwork ATS in real-time and stream results to the UI with zero loading states
API backends: create serverless endpoints that orchestrate Flatwork ATS tools and return structured JSON responses to any frontend
Chatbots with tool use: embed Flatwork ATS capabilities into conversational interfaces with streaming responses and tool call visibility
Internal tools: build admin panels where team members interact with Flatwork ATS through natural language queries
Example Prompts for Flatwork ATS in Vercel AI SDK
Ready-to-use prompts you can give your Vercel AI SDK agent to start working with Flatwork ATS immediately.
"List all active job postings in Flatwork ATS."
"Add 'John Doe' (john.doe@example.com) as a new applicant."
"Move application ID 'app_987' to the 'Interview' stage."
Troubleshooting Flatwork ATS MCP Server with Vercel AI SDK
Common issues when connecting Flatwork ATS to Vercel AI SDK through Vinkius, and how to resolve them.
createMCPClient is not a function
npm install @ai-sdk/mcpFlatwork ATS + Vercel AI SDK FAQ
Common questions about integrating Flatwork ATS MCP Server with Vercel AI SDK.
How does the Vercel AI SDK connect to MCP servers?
createMCPClient from @ai-sdk/mcp and pass the server URL. The SDK discovers all tools and provides typed TypeScript interfaces for each one.Can I use MCP tools in Edge Functions?
Does it support streaming tool results?
useChat and streamText that handle tool calls and display results progressively in the UI.