How to Use the Magento (Adobe Commerce) MCP in LlamaIndex

Q: Can LlamaIndex query Magento (Adobe Commerce) configs directly?

Yes, use mg.getstoreconfig to pull current settings. This data becomes part of your searchable index, allowing the agent to reference store rules.

Index live Magento (Adobe Commerce) data into your LlamaIndex knowledge base for semantic retrieval.

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Connect Magento (Adobe Commerce) MCP to LlamaIndex

Create your Vinkius account to connect Magento (Adobe Commerce) 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 Magento (Adobe Commerce) with LlamaIndex

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Semantic search over Magento (Adobe Commerce) records

Convert your store data into a queryable index by feeding `mg.search_products` results into your vector store. LlamaIndex then allows you to ask questions about your catalog in plain English. Ground your AI responses in real-time inventory and catalog data. This prevents hallucinations by forcing the model to query the source of truth before answering.

Build RAG pipelines with live commerce data

Combine your documents with live API results from `mg.get_store_config` and `mg.list_invoices`. LlamaIndex manages the chunking and retrieval while this MCP Server handles the API communication. Update your index dynamically as new orders hit the platform. The agent pulls the latest data to ensure your knowledge base never goes stale.

Ground AI answers in actual API responses

Use the `mg.get_product` tool to fetch specific SKU details for your RAG application. By including these tools in your tool list, you bridge the gap between static documents and dynamic store data. The server returns structured data that LlamaIndex parses into semantic nodes. This makes your store's configuration and product info immediately searchable.

Setup guide

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

Use the McpToolSpec class to convert server tools into a format LlamaIndex accepts. Once converted, pass them to your FunctionAgent for immediate use.

Yes, the tools are designed to output data that maps directly into your indexing pipeline. You can add the MCP Server as an additional data source.

Yes, use `mg.get_store_config` to pull current settings. This data becomes part of your searchable index, allowing the agent to reference store rules.

You only need the endpoint token provided by Vinkius. The MCP client handles the header injection automatically.

The server only retrieves the specific order and customer data you request via `mg.get_order` or `mg.search_orders`. This data stays within your local environment and is never logged by the server itself.

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