How to Use the NeetoInvoice MCP in Vercel AI SDK

Q: Can I use NeetoInvoice data to update Vercel AI SDK state?

Yes. The server returns structured data from tools like listtimeentries that your frontend can parse and inject into your application state.

Stream NeetoInvoice billing data directly into your Vercel AI SDK frontends for live, interactive user experiences.

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Connect NeetoInvoice MCP to Vercel AI SDK

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

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Real-time billing in Vercel AI SDK

Your frontends can pull live data from NeetoInvoice using `list_time_entries` and display it as it loads. Users see hours and project status update instantly within your React or Next.js components. Stop waiting for spinners. By plugging this MCP Server into your SDK, you get raw JSON results that stream straight to your UI layer without extra middleware.

Automated invoice creation for your app

Trigger `generate_invoice` directly from your client-side events. Your users can request a bill draft and see the result rendered in the browser immediately. Keep your logic tight. Using `get_client` alongside invoice calls ensures you have the right data ready before the user clicks confirm.

Manage projects inside Vercel AI SDK

Update project details or add users on the fly using `update_project` and `add_project_user`. Your interface stays in sync with your backend billing state. It handles the heavy lifting. You define the flow, and the server executes the tool calls against your workspace without leaving your deployment environment.

Setup guide

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

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Common questions about NeetoInvoice MCP in Vercel AI SDK

Install the MCP packages and initialize the client with your Vinkius endpoint. You then pass the tools directly into your `generateText` calls.

Yes. The server returns structured data from tools like `list_time_entries` that your frontend can parse and inject into your application state.

It does. The HTTP transport ensures your edge functions can reach the server endpoint with minimal latency.

Your data is scoped to your specific endpoint token. We use a zero-trust sandbox for every request made by your client.

The server returns a standard error code that you can catch in your SDK handlers. You should implement basic retry logic for network-level blips.

Use it with your favorite AI tools

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

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