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How to Use the Searchspring MCP in Vercel AI SDK

Stream live e-commerce search results from Searchspring directly into your Vercel AI SDK app's UI.

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Works with every AI agent you already use

…and any MCP-compatible client

Searchspring MCP on Cursor AI Code Editor MCP Client Searchspring MCP on Claude Desktop App MCP Integration Searchspring MCP on OpenAI Agents SDK MCP Compatible Searchspring MCP on Visual Studio Code MCP Extension Client Searchspring MCP on GitHub Copilot AI Agent MCP Integration Searchspring MCP on Google Gemini AI MCP Integration Searchspring MCP on Lovable AI Development MCP Client Searchspring MCP on Mistral AI Agents MCP Compatible Searchspring MCP on Amazon AWS Bedrock MCP Support
MCP Servers — Included with Plan
Vinkius runs on Vercel AI SDK

Connect Searchspring MCP to Vercel AI SDK

Create your Vinkius account to connect Searchspring to Vercel AI SDK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

GDPR Included with Plan

Key Capabilities

Build Live Product Search

Connect your Vercel AI SDK app to the Searchspring catalog. Your agent can run a `search_products` query based on user input, and results stream into your React components in real time. No more loading spinners for simple searches. For more complex UIs, use `search_filtered` to apply facets like size or color, and `search_sorted` to reorder by price. The AI client handles the query, and the `ai` package streams the updated product list back to the user instantly from the MCP server.

Power Real-Time Suggestions

This server gives your agent the `suggest_queries` tool. As a user types into a search bar, your Vercel AI SDK app can send the partial input to your agent, which then gets relevant search suggestions from Searchspring. These suggestions aren't just static text; they are live recommendations based on the actual catalog. The result streams back, letting you build a fast, responsive autocomplete feature that helps customers find what they're looking for.

MCP Server for Product Details

When a user clicks a product, you don't need a separate API call. Your AI client can use the `search_sku` tool from this MCP server to pull full details for that one item. It’s a direct lookup, so it’s fast. You can also construct brand or category pages on the fly. Just have the agent call `search_brand` or `search_category`. The Vercel AI SDK will stream the product data right into your page components as it arrives.

Setup guide

Set up Searchspring 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. 1

    Install dependencies

    Run npm install ai @modelcontextprotocol/sdk plus your preferred model provider (e.g. @ai-sdk/openai).

  2. 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. 3

    Discover and use tools

    Call mcpClient.tools() to auto-discover all Searchspring tools. Pass them directly to generateText() or streamText() — no manual schema definitions needed.

  4. 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 Searchspring transactions",
});

console.log(text);
await mcpClient.close();

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Searchspring. 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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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 Searchspring MCP in Vercel AI SDK

You'll use the `search_filtered` tool. Pass a string of key-value pairs like "color:blue,size:large" to your agent, and the Vercel AI SDK streams the filtered product results from the Searchspring MCP server right into your UI.
Yes, that's what the `suggest_queries` tool is for. As the user types, your app sends the partial text to the agent. It then uses the tool to get autocomplete suggestions from the Searchspring catalog.
Give your agent the `search_brand` tool. It takes the brand name as input and returns a list of matching products from the Searchspring catalog, which you can then stream into your Next.js or React components.
The `search_pagination` tool handles it. Tell your agent the page number and items per page. It gets the correct slice of results from the Searchspring API.
This server only reads public product catalog data from Searchspring—things like SKUs, prices, and brand names. It doesn't handle or see any user PII. All connections are ephemeral and run in a sandboxed environment.

Start using the Searchspring MCP today

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