How to Use the Klevu (E-commerce AI Search) MCP in VS Code Copilot

Q: What is the best way to execute raw API queries?

You use searchraw to send custom JSON payloads directly to Klevu. This is ideal for testing complex search configurations that go beyond standard keyword or category queries.

Share live Klevu (E-commerce AI Search) catalog tools across your entire engineering team in VS Code Copilot.

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Connect Klevu (E-commerce AI Search) MCP to VS Code Copilot

Create your Vinkius account to connect Klevu (E-commerce AI Search) to VS Code Copilot 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 VS Code Copilot

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Standardize catalog testing with this MCP Server

`search_keyword` executes direct catalog searches using Klevu's search engine directly from your VS Code Copilot chat window. Committing this MCP Server configuration to your repository lets all developers query the live store catalog without leaving the editor. This setup keeps your team aligned on API structures. Developers can instantly verify product availability, description formats, and pricing fields without opening a browser.

Verify collection page layouts with live merchandising

`search_category` retrieves the exact product sequence for your store's collection pages using Klevu's smart category merchandising. Your Copilot agent uses this data to verify that your layout code handles the exact sequence of products returned by the server. If you need to test paginated layouts, `search_pagination` fetches subsequent pages of products. This ensures your infinite scroll or pagination components work flawlessly with the live API.

Test recommendations and filters in your shared workspace

`search_recs` pulls real-time product recommendations based on specific product IDs. Your team can use Copilot to write recommendation carousel components using the exact JSON structure returned by the live recommendation engine. You can also test complex filter states using `search_filtered`. This tool returns product sets matching specific facets, making it easy to write unit tests for your sidebar filter components with this MCP tool.

Setup guide

Set up Klevu (E-commerce AI Search) MCP in VS Code Copilot

Prerequisites

VS Code 1.99 or later with GitHub Copilot extension
Active Vinkius subscription with a valid endpoint token

1

Open MCP configuration

Open the Command Palette (Cmd+Shift+P / Ctrl+Shift+P) and run "MCP: Add Server". Select HTTP (Streamable) as the server type. VS Code will create .vscode/mcp.json in your workspace.
2

Add the Klevu (E-commerce AI Search) MCP

Paste the JSON snippet shown on the right into your .vscode/mcp.json. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Switch to Agent mode

Open Copilot Chat (Cmd+Shift+I / Ctrl+Shift+I) and switch to Agent mode using the dropdown. MCP tools are only available in Agent mode — they do not appear in Edit or Ask modes.
4

Verify the connection

In the Copilot Chat input, type # to list available tools. You should see the Klevu (E-commerce AI Search) tools listed. Try asking: "List my recent Klevu (E-commerce AI Search) transactions" and Copilot will invoke them automatically.

.vscode/mcp.json

{
  "mcpServers": {
    "klevu-e-commerce-ai-search-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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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 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 VS Code Copilot

Your team members can ask Copilot Chat to search for products. The agent invokes `search_keyword` to fetch live data from your Klevu index, helping developers write UI code with real product payloads.

Yes. Ask your Copilot agent to load a specific category page, which triggers `search_category`. The agent retrieves the merchandised product list so you can verify frontend layout rendering.

You ask Copilot to generate a recommendation component. It calls `search_recs` to fetch real recommendations, using the actual API response schema to write clean, typed frontend code.

You use `search_raw` to send custom JSON payloads directly to Klevu. This is ideal for testing complex search configurations that go beyond standard keyword or category queries.

Every search query and product ID processed by this MCP Server runs inside an isolated Vinkius container. No customer database records or sensitive transaction logs are accessed, keeping your customer search behavior completely private.

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