How to Use the VTEX Catalog MCP in LlamaIndex

Q: How does LlamaIndex handle complex searches within VTEX Catalog?

LlamaIndex indexes the outputs from tools like searchproducts. You can then query this index using natural language, asking questions that relate to product features or brands without needing specific IDs.

Q: Can I use LlamaIndex for historical data analysis on VTEX Catalog?

Absolutely. Every time the agent runs getproduct or checks stock via managestock, that result gets saved to a searchable knowledge base, letting you query past sessions.

Q: What types of data can I index from VTEX Catalog using LlamaIndex?

You can index product descriptions, brand details (getbrand), category hierarchies (getcategory), and specific SKU records. Anything the MCP Server outputs is a candidate for indexing.

Index VTEX Catalog results for semantic search using LlamaIndex.

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Connect VTEX Catalog MCP to LlamaIndex

Create your Vinkius account to connect VTEX Catalog 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 VTEX Catalog with LlamaIndex

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LlamaIndex turns tool outputs into searchable vector embeddings. If you run `get_product` multiple times, the resulting details aren't just shown; they become part of a unified index. You can query past API results for answers grounded in actual VTEX Catalog data. This is huge because it means your agent doesn't hallucinate product specs—it pulls them from the indexed records.

Search products using LlamaIndex

You can use `search_products` and then index those results. Later, you can ask a question like, 'What were the specifications of the Coca Cola items we looked at last week?' The knowledge base finds the answer based on semantic meaning, not just keyword matching. It makes deep, historical analysis possible using LlamaIndex.

Manage and track catalog changes with MCP Server

When you use tools like `manage_stock` or `save_product`, the resulting state change is indexed. This allows your application to query not just *what* a product is, but *how* its inventory was changed over time. This capability makes compliance and auditing straightforward by keeping all API transaction results in the knowledge graph.

Setup guide

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

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Common questions about VTEX Catalog MCP in LlamaIndex

LlamaIndex indexes the outputs from tools like `search_products`. You can then query this index using natural language, asking questions that relate to product features or brands without needing specific IDs.

Absolutely. Every time the agent runs `get_product` or checks stock via `manage_stock`, that result gets saved to a searchable knowledge base, letting you query past sessions.

You can index product descriptions, brand details (`get_brand`), category hierarchies (`get_category`), and specific SKU records. Anything the MCP Server outputs is a candidate for indexing.

It grounds answers in the data provided by the server, meaning it finds documented facts from the catalog rather than generating speculative text. This keeps your results accurate and verifiable.

The core data types include product names, descriptions, brand IDs, category paths, and quantitative stock levels associated with each SKU.

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