How to Use the Unanet MCP in Vercel AI SDK

Q: How do I check project details with Unanet and Vercel AI SDK?

You call projects() to get a list of all available projects in Unanet. This data streams directly into your front end, giving the user immediate visibility without extra API calls.

Q: Can I handle expense report fetching with Unanet and Vercel AI SDK?

Yeah, you use expenses() to fetch reports for a specific person. The results stream in real time, making it look like the data is appearing instantly on your page.

Q: What kind of employee records does Unanet and Vercel AI SDK support?

The server uses the users() tool to pull basic profile information for all employees in Unanet. This allows you to build out user-specific features right into your React application.

Q: What specific data types are exposed by this MCP Server for privacy considerations?

The server exposes user profile details via users(), project metadata via projects(), and financial records including time logs and expense reports. Always ensure your client handles these different data streams securely.

Stream Unanet Data Live into Your UI with Vercel AI SDK.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Unanet MCP to Vercel AI SDK

Create your Vinkius account to connect Unanet 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.

GDPR Free for Subscribers

Setup Unanet with Vercel AI SDK

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Fetch Employee Roster for your Vercel AI SDK App

Need a list of all employees? Call `users()` to pull an instant roster of every user in Unanet. This data streams right into the UI, letting your end-user see the full employee count appear as it loads. You'll get real-time data updates without having to rely on clunky loading spinners. It’s perfect for building client dashboards that need immediate feedback.

Track Project Status with Unanet and Vercel AI SDK

Your agent needs project details? Use the `projects()` tool. This gives you a list of all active projects within Unanet, showing key identifiers for each one. This capability is great when building complex interfaces where the user needs to check multiple project statuses live in their browser.

Calculate Total Time and Expenses via MCP Server

Figure out how much time or money was spent? You can call `timesheets()` and `expenses()`. These tools provide the detailed records for a specific user, listing every entry. You get two distinct data streams—time logs and expense reports—which you can render separately in your Next.js component using the streaming endpoint.

Setup guide

Set up Unanet 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 Unanet 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 Unanet 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 Unanet. 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

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Unanet now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Unanet MCP in Vercel AI SDK

You call `projects()` to get a list of all available projects in Unanet. This data streams directly into your front end, giving the user immediate visibility without extra API calls.

Yeah, you use `expenses()` to fetch reports for a specific person. The results stream in real time, making it look like the data is appearing instantly on your page.

The server uses the `users()` tool to pull basic profile information for all employees in Unanet. This allows you to build out user-specific features right into your React application.

Absolutely. Because it streams data, the client only gets chunks of information as they become available. This method handles large result sets much better than traditional loading patterns.

The server exposes user profile details via `users()`, project metadata via `projects()`, and financial records including time logs and expense reports. Always ensure your client handles these different data streams securely.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript