How to Use the NCR Voyix MCP in LlamaIndex

Index your NCR Voyix commerce data into LlamaIndex to build RAG apps that ground answers in real-time retail metrics.

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Connect NCR Voyix MCP to LlamaIndex

Create your Vinkius account to connect NCR Voyix 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 NCR Voyix with LlamaIndex

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Ground LlamaIndex queries in live retail data

Turn your API output into a searchable knowledge base. By passing `list_products` through an indexer, your agent can answer questions about your catalog using current data. This removes the risk of stale information. Your LlamaIndex application references the latest server state instead of relying on outdated training sets.

Contextual order search for LlamaIndex

Find specific commerce transactions by indexing order history. Use `list_orders` to feed your vector store, allowing the agent to recall specific customer interactions. It makes your retrieval process faster. Instead of scanning flat lists, the agent queries the index to find the exact `get_order` details you need.

Smart site indexing

Map your retail locations into a structured knowledge index. Use `list_sites` to define your scope and `get_site` to retrieve the specifics for each node. Your agent can now answer questions about site-specific inventory or store hours. It links site metadata to your semantic search queries for better accuracy.

Setup guide

Set up NCR Voyix 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 NCR Voyix 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 NCR Voyix tools.",
)
response = await agent.run("List recent NCR Voyix data")

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Common questions about NCR Voyix MCP in LlamaIndex

Use the MCP tool spec to pull data and pass it to your document loader. This converts the API output into a format your vector store can digest.

You need to trigger the indexing pipeline when you want fresh data. It won't poll the server on its own unless you build a recurring task in your agent.

Absolutely. Once the data is indexed, you can ask natural language questions about your products and orders, and the agent will return grounded answers.

The data lives where you point your vector index. You control the encryption and access rights for the resulting knowledge base.

It provides structured JSON for orders, products, and site configurations. These are perfect for semantic indexing and retrieval tasks.

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