How to Use the commercetools MCP in LlamaIndex

Index your commercetools catalog into LlamaIndex for grounded, accurate RAG responses.

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Connect commercetools MCP to LlamaIndex

Create your Vinkius account to connect commercetools 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 commercetools with LlamaIndex

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Ground LlamaIndex in commercetools data

Convert API responses into searchable vector embeddings. You index `list_categories` or `list_products` so your agent references real store catalog data instead of guessing. This MCP Server turns your static store data into a queryable knowledge base. Your RAG pipeline retrieves the exact product details required for your user's specific request.

Query past sessions in LlamaIndex

Store your API interactions as indexed documents for future retrieval. You ask your agent about previous order trends, and it pulls the answer from your vector store. This allows you to build a history of commerce activity. You get precise answers backed by historical API records rather than generic summaries.

Build semantic search for commercetools

Map your product keys and descriptions into a searchable index. You use `search_product_by_key` to feed your LlamaIndex engine with granular, high-fidelity data. Your agent performs semantic lookups across your entire inventory. It finds items by intent rather than just matching exact keywords.

Setup guide

Set up commercetools 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 commercetools 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 commercetools tools.",
)
response = await agent.run("List recent commercetools 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 commercetools. 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 commercetools MCP in LlamaIndex

The MCP Server pipes raw API output into your document loaders. You then define the index structure in LlamaIndex to represent your catalog hierarchy.

You pass the tool list directly to the FunctionAgent. It executes calls against your project and indexes the results into your vector store.

You trigger a re-index whenever your store data changes. The MCP Server provides the fresh data needed to keep your vector store current.

You only need the standard connector. It handles the schema mapping so you can focus on building your retrieval logic.

Data is processed in an ephemeral sandbox. Your customer names and addresses are never stored in the index permanently unless you explicitly configure the vector store to persist them.

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