How to Use the TurfHop MCP in Vercel AI SDK

Q: How does TurfHop integrate with Vercel AI SDK?

You use the mcpClient.tools() method within your agent setup. This allows the client to expose all 12 operational tools directly into your streaming interface, making them available for calls.

Q: Can I update records using TurfHop with Vercel AI SDK?

Yep. You can call tools like updatecustomer or updatejob. The agent executes the change, and you stream back confirmation to your user in real time.

See real-time operational data streams using your AI client in Vercel AI SDK.

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Connect TurfHop MCP to Vercel AI SDK

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

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Build live, streaming UIs with TurfHop's customer tools.

Your agent can use the `list_customers` tool to grab a roster of clients. Then, it runs `get_customer` by ID, piping that data directly into your React component as the user watches. This eliminates those annoying loading spinners. Because the results stream live, you don't have to wait for a massive JSON blob. The end-user sees the customer name and address appear instantly in the UI.

Process job details instantly with streaming MCP Server calls.

When an admin needs to check on a service call, your agent can call `get_job` using a specific job ID. The returned data streams into your front end—showing the status and notes as they arrive. This direct stream capability means you build products where the user sees the AI working live in the browser, making the whole process feel faster.

Manage invoices by calling `list_invoices` directly from your client.

The agent handles fetching billing records; it just calls `list_invoices`. You then take that list and display it in a table on your webpage. The data appears piece by piece, not all at once. This design makes building user-facing tools simple because the result of the MCP Server shows up right when you expect it to.

Setup guide

Set up TurfHop 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 TurfHop 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 TurfHop 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 TurfHop. 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 TurfHop MCP in Vercel AI SDK

You use the `mcpClient.tools()` method within your agent setup. This allows the client to expose all 12 operational tools directly into your streaming interface, making them available for calls.

You can fetch core business information like customer details via `get_customer`, or get a list of products and services by calling `list_products`. All this data streams into your client.

Yep. You can call tools like `update_customer` or `update_job`. The agent executes the change, and you stream back confirmation to your user in real time.

Just use the MCP Server's tools. You can get invoice details by calling `get_invoice` or listing all past bills with `list_invoices`. The data streams right to your UI.

The server handles customer records, including names and addresses. It also manages job details, which include service descriptions and statuses.

Use it with your favorite AI tools

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