How to Use the Kavak MCP in Vercel AI SDK

Q: How do I connect the Kavak MCP Server to the Vercel AI SDK?

Install @ai-sdk/mcp and use createMCPClient with your HTTP transport URL. Pass the resulting tools directly into streamText to start pulling car data. Always remember to call mcpClient.close() when the session ends.

Q: Can the Vercel AI SDK handle Kavak trade-in valuations?

Yes. The agent calls estimatetradein with the user's car details. It calculates the value based on the Mexican market and streams the pricing estimate straight to your frontend.

Q: How does Vercel AI SDK process Kavak reservations?

You trigger the createreservation tool using the buyer's contact info. This executes the hold on the Kavak side and returns a confirmation you can render instantly in your React app.

Q: Can I calculate financing options during a chat?

You just invoke getfinancingoptions for any specific vehicle ID. The agent pulls the math and displays the monthly breakdown without breaking the conversational flow.

Stream live Mexican used car market data and 240-point inspection reports straight into your Next.js frontend using the Vercel AI SDK.

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Connect Kavak MCP to Vercel AI SDK

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

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Stream Kavak inventory directly to users

Your Vercel AI SDK setup hits the `search_vehicles` tool to pull live used car inventory from Kavak's database. Instead of making buyers wait for a bulky JSON payload, you stream the available makes and models instantly to the UI. Combine `list_makes` and `list_models` to build dynamic search filters in your React components. The MCP Server ensures you are always pulling fresh data, so your buyers never look at cars that already sold.

Render 240-point inspections in real-time

The `get_vehicle_inspection` tool grabs the exact mechanical report for any specific car on the lot. Your agent can read the report and instantly stream a plain-English summary of the vehicle's condition right to the chat interface. If a buyer asks about past accidents, call `get_vehicle_history` to pull ownership records. You give them the hard facts immediately, building trust without forcing them to click through ten different web pages.

Vercel AI SDK checkout and reservations

Your agent executes `create_reservation` the moment a buyer makes a decision. The tool requires basic contact details and instantly pulls the vehicle off the public marketplace. Once the car is locked down, you can hit `schedule_test_drive` to arrange a home delivery or a visit to a local hub. Use `list_locations` to map out the closest showroom, closing the loop completely within your application.

Setup guide

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

Install `@ai-sdk/mcp` and use `createMCPClient` with your HTTP transport URL. Pass the resulting tools directly into `streamText` to start pulling car data. Always remember to call `mcpClient.close()` when the session ends.

Yes. The agent calls `estimate_trade_in` with the user's car details. It calculates the value based on the Mexican market and streams the pricing estimate straight to your frontend.

You trigger the `create_reservation` tool using the buyer's contact info. This executes the hold on the Kavak side and returns a confirmation you can render instantly in your React app.

You just invoke `get_financing_options` for any specific vehicle ID. The agent pulls the math and displays the monthly breakdown without breaking the conversational flow.

When you execute holds, buyer names and phone numbers route securely through the V8 Isolate Sandbox directly to the endpoint. Vinkius drops the ephemeral container immediately after the transaction, leaving zero trace of the personal information on our infrastructure.

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