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

Q: How do I index trending products from Klevu (E-commerce AI Search) into LlamaIndex using the MCP Server?

You call searchtrending within your LlamaIndex pipeline to fetch the latest popular items. The tool output is converted into document nodes and indexed for semantic retrieval.

Q: Can LlamaIndex query specific product details by ID?

Yes, it uses searchproductid to pull exact specs for a product. This ensures your agent has the correct description and pricing before answering a shopper's question.

Q: How do I apply faceted filters using Klevu (E-commerce AI Search) in my LlamaIndex pipelines?

You run searchfiltered to narrow down the catalog based on attributes like size or brand. LlamaIndex then indexes only this filtered subset of products for the user's session.

Q: Can I run custom search payloads against Klevu (E-commerce AI Search) in LlamaIndex using this MCP Server?

Yes, you can execute raw queries using searchraw. This lets your LlamaIndex application bypass the standard wrapper when you need to run complex, nested JSON queries.

Index live Klevu search results directly into your LlamaIndex vector stores for hallucination-free retail RAG.

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Connect Klevu (E-commerce AI Search) MCP to LlamaIndex

Create your Vinkius account to connect Klevu (E-commerce AI Search) to LlamaIndex 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 LlamaIndex

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Build a retail RAG with LlamaIndex

This MCP Server feeds live product data directly into your LlamaIndex knowledge base. By calling `search_keyword`, your agent pulls real-time inventory and indexes it instantly. This prevents your retail agent from hallucinating old prices or out-of-stock items. The agent queries the index, ensuring every recommendation matches what is actually on your shelves.

Index category pages dynamically

You can index entire collections by feeding `search_category` output into LlamaIndex documents. The agent parses the category structure and builds a local vector representation of your store layout. When shoppers ask complex questions about your inventory, LlamaIndex matches their intent against this indexed category data. This makes finding specific product groups faster and more accurate.

Personalize recommendations from indexed data

Combine live user behavior with Klevu tools to serve highly relevant recommendations. Your LlamaIndex pipeline can fetch trending items using `search_trending` and merge them with local shopper profiles. The agent then runs `search_recs` to find matching items that fit the shopper's current session. This creates a deeply personalized search experience grounded in real-time retail data.

Setup guide

Set up Klevu (E-commerce AI Search) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Klevu (E-commerce AI Search) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Klevu (E-commerce AI Search) tools.",
)
response = await agent.run("List recent Klevu (E-commerce AI Search) data")

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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 LlamaIndex

You call `search_trending` within your LlamaIndex pipeline to fetch the latest popular items. The tool output is converted into document nodes and indexed for semantic retrieval.

Yes, it uses `search_product_id` to pull exact specs for a product. This ensures your agent has the correct description and pricing before answering a shopper's question.

You run `search_filtered` to narrow down the catalog based on attributes like size or brand. LlamaIndex then indexes only this filtered subset of products for the user's session.

Yes, you can execute raw queries using `search_raw`. This lets your LlamaIndex application bypass the standard wrapper when you need to run complex, nested JSON queries.

Absolutely. The integration runs in an ephemeral, zero-trust environment. Your product names, descriptions, and SKUs are processed in memory without being permanently stored on our servers.

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