How to Use the Klevu (E-commerce AI Search) MCP in Vercel AI SDK

Q: How do I handle pagination when using Klevu (E-commerce AI Search) with Vercel AI SDK?

You use the searchpagination tool inside your stream. The SDK handles the raw tool output, allowing your agent to request the next page of products when a user clicks a load-more button or asks for more results.

Q: How do I render custom product layouts with Klevu (E-commerce AI Search) and Vercel AI SDK?

Use searchproductid to fetch detailed product data. You can map this structured JSON output directly to your custom React components inside the streamText helper, creating a tailored product card layout instantly.

Q: What if I need to run a complex query that isn't covered by standard tools?

You can fall back to the searchraw tool. This tool lets your agent construct and execute custom JSON payloads directly against the backend API, giving you complete control over the search parameters.

Stream real-time Klevu product search and recommendations directly into your Next.js UI using the Vercel AI SDK.

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Connect Klevu (E-commerce AI Search) MCP to Vercel AI SDK

Create your Vinkius account to connect Klevu (E-commerce AI Search) 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 Klevu (E-commerce AI Search) with Vercel AI SDK

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Instant search suggestions using Vercel AI SDK

Your users expect instant feedback when they start typing in a search bar. By hooking up `search_autocomplete` to your streaming edge functions, your AI client can instantly populate dropdowns with smart suggestions. You don't have to write custom polling logic or manage complex state machines. The tool feeds raw suggestion data right into your frontend stream. This means you can render autocomplete options instantly as the user types, keeping latency low and the interface highly responsive.

UI-driven product discovery with MCP Server tools

Let your agent build dynamic collection pages on the fly. By exposing `search_category` and `search_filtered`, your application lets the LLM filter down products by size, color, or brand based on conversational prompts. Instead of rendering static lists, the Vercel AI SDK streams these refined product lists directly into your React components. The user gets a tailored shopping experience that updates right before their eyes as the tool executes.

Personalized recommendations on the edge

Drop `search_recs` and `search_trending` directly into your edge-compatible routes. This setup lets your client fetch trending items or personalized product recommendations without hitting heavy database queries. It keeps the initial page load incredibly fast. Because this runs on Vinkius's low-latency MCP server infrastructure, the recommendations render alongside your streaming UI components without blocking the main thread.

Setup guide

Set up Klevu (E-commerce AI Search) 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 Klevu (E-commerce AI Search) 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 Klevu (E-commerce AI Search) 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 Klevu. 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 Klevu (E-commerce AI Search) MCP in Vercel AI SDK

You use the `search_pagination` tool inside your stream. The SDK handles the raw tool output, allowing your agent to request the next page of products when a user clicks a load-more button or asks for more results.

Yes, absolutely. The Vinkius MCP host handles the HTTP transport, meaning you can call `search_keyword` from lightweight environments like Vercel Edge Functions without worrying about complex dependencies or cold starts.

Use `search_product_id` to fetch detailed product data. You can map this structured JSON output directly to your custom React components inside the streamText helper, creating a tailored product card layout instantly.

You can fall back to the `search_raw` tool. This tool lets your agent construct and execute custom JSON payloads directly against the backend API, giving you complete control over the search parameters.

All search queries and catalog metadata are routed through secure, ephemeral sandboxes. No customer data is stored on our servers, and the connection uses token-based authentication to keep your storefront secure.

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