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

Q: How do I handle empty search results using the Klevu (E-commerce AI Search) MCP Server in LangChain?

You set up a fallback chain. If searchkeyword returns zero items, your LangChain agent automatically calls searchautocomplete to suggest alternative terms to the shopper.

Q: Can I pass raw JSON payloads to Klevu (E-commerce AI Search) from a LangChain chain?

Yes, you can use the searchraw tool. This lets your LangChain agent execute custom Klevu API payloads when standard tools don't cover your specific use case.

Build multi-step e-commerce search chains by connecting your LangChain agents directly to Klevu.

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

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

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Chain search queries with LangChain

Connect this MCP Server to your LangChain setup to handle search requests by linking different Klevu tools together. Your agent can trigger `search_keyword` to find products and immediately feed those IDs into `search_recs` to pull up related items in a single run. You get full visibility into this chain through LangSmith tracing. Every tool execution shows exact token usage and latency, so you see exactly how the agent refines its search intent.

Dynamic filter extraction in chains

LangChain agents can parse complex, messy user questions and map them to structured Klevu parameters. The agent extracts sizes, colors, or brands from natural language and passes them directly to `search_filtered`. If the results are too narrow, the agent uses `search_pagination` or falls back to `search_autocomplete` to suggest alternatives. This keeps the shopping flow moving without breaking the chain.

Merchandising via MCP Server tools

This MCP Server lets your agent manage category pages on the fly based on user conversation. By calling `search_category`, the agent fetches smart merchandising rules directly from Klevu. You can combine this with `search_trending` to inject popular global items into the category view. This lets your LangChain setup build personalized landing pages based on real-time trends.

Setup guide

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

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Klevu (E-commerce AI Search) tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "klevu-e-commerce-ai-search-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Klevu (E-commerce AI Search) transactions"
    })
    print(result["messages"][-1].content)

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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 LangChain

You set up a fallback chain. If `search_keyword` returns zero items, your LangChain agent automatically calls `search_autocomplete` to suggest alternative terms to the shopper.

Yes, you can track everything. LangSmith traces every call to `search_filtered` or `search_recs`, showing you the exact execution time and payloads.

The agent uses its routing logic. If a shopper asks for a general term, it triggers `search_keyword`, but if they navigate to a department, it switches to `search_category`.

Yes, you can use the `search_raw` tool. This lets your LangChain agent execute custom Klevu API payloads when standard tools don't cover your specific use case.

Vinkius runs this MCP Server in an isolated, zero-trust sandbox. Your search queries, product IDs, and category structures are never stored or used for training, keeping your catalog data private.

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