How to Use the Medusa (Headless E-commerce Engine) MCP in LlamaIndex

Q: How do I index Medusa (Headless E-commerce Engine) catalogs into LlamaIndex?

Use McpToolSpec to wrap the listproducts tool. LlamaIndex executes the tool, retrieves the raw JSON catalog, and indexes the text nodes directly into your vector store.

Q: Can LlamaIndex query Medusa (Headless E-commerce Engine) order history to prevent hallucinations?

Yes. By calling getorder, the agent grounds its responses in actual order history data. The system reads the live fulfillment status, ensuring it never invents shipping updates.

Q: How do I restrict which Medusa (Headless E-commerce Engine) tools LlamaIndex can access?

You can apply the allowedtools filter when initializing your McpToolSpec on this MCP Server. This lets you expose safe tools like listproducts while blocking sensitive write operations.

Q: What python packages do I need to run this MCP Server with LlamaIndex?

Install the connector using pip install llama-index-tools-mcp. Once installed, initialize BasicMCPClient with your Vinkius endpoint to start loading the tools.

Index live store data from Medusa (Headless E-commerce Engine) into LlamaIndex vector stores for accurate, context-rich RAG.

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Connect Medusa (Headless E-commerce Engine) MCP to LlamaIndex

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Setup Medusa (Headless E-commerce Engine) with LlamaIndex

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Index product data for LlamaIndex RAG

The `list_products` tool fetches your catalog structure via this MCP Server to feed directly into LlamaIndex document stores. By parsing these raw payloads, the framework builds a semantic index of your inventory. Users query your system about product features without relying on static databases. LlamaIndex matches their search terms against the active catalog data retrieved by `get_product`.

Ground customer queries in live order data

The `get_order` tool provides the exact line items and fulfillment status needed to ground user queries. By indexing this data, LlamaIndex ensures your agent answers shipping questions using real-time facts instead of guessing. This setup avoids the hallucination issues common in standard LLM deployments. When a customer asks about a delivery, the system pulls the tracking state directly from `list_orders`.

Query store configurations with LlamaIndex and MCP

This MCP Server lets you query regional settings using `list_regions` to adjust tax and currency calculations dynamically. Using this context, LlamaIndex formats pricing information for international buyers. You can also call `get_store_config` to identify the default region before running a search. This ensures the retrieved product prices match the user's local currency.

Setup guide

Set up Medusa (Headless E-commerce Engine) 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 Medusa (Headless E-commerce Engine) 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 Medusa (Headless E-commerce Engine) tools.",
)
response = await agent.run("List recent Medusa (Headless E-commerce Engine) data")

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Common questions about Medusa (Headless E-commerce Engine) MCP in LlamaIndex

Use `McpToolSpec` to wrap the `list_products` tool. LlamaIndex executes the tool, retrieves the raw JSON catalog, and indexes the text nodes directly into your vector store.

Yes. By calling `get_order`, the agent grounds its responses in actual order history data. The system reads the live fulfillment status, ensuring it never invents shipping updates.

You can apply the `allowed_tools` filter when initializing your `McpToolSpec` on this MCP Server. This lets you expose safe tools like `list_products` while blocking sensitive write operations.

Install the connector using `pip install llama-index-tools-mcp`. Once installed, initialize `BasicMCPClient` with your Vinkius endpoint to start loading the tools.

All communication flows through an encrypted HTTPS connection directly to the V8 sandbox. Your customer profiles and order history are processed entirely in memory, with zero persistent storage on the Vinkius platform.

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