How to Use the Zid MCP in LlamaIndex

Q: How does LlamaIndex use Zid to find customer details?

You call getzidcustomer and then pass the resulting customer record into the indexing pipeline. This allows subsequent queries to semantically search not just by ID, but by attributes within that customer's history.

Q: Can LlamaIndex help me find product information using Zid?

Absolutely. By running listzidproducts, the resulting product catalog data gets indexed. You can then query natural language questions like, 'Which products are currently out of stock?' and get an answer based on the API response.

Q: What if I need to track multiple orders using Zid and LlamaIndex?

You run listzidorders and include those results in your index. When you query, the system searches across all indexed order records simultaneously, giving a comprehensive view of fulfillment status.

Q: How do I make sure LlamaIndex uses the latest inventory data from Zid?

You must run listzidinventories and include those results in your index before querying. The RAG process is only as current as the data you feed it.

Q: If I need to check shop status using Zid and LlamaIndex, what data type should I focus on?

You'll want to index the 'API Status' data. The checkzidstatus tool provides a clean boolean or text result that can be easily searched and verified in your knowledge base.

Ground e-commerce answers in Zid's API data using LlamaIndex.

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Setup Zid with LlamaIndex

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Building RAG applications with LlamaIndex and the MCP Server

LlamaIndex allows you to index live results from the Zid e-commerce platform. Instead of just getting a list of orders via `list_zid_orders`, that output becomes searchable knowledge. You can then query past sessions or configurations against that data. This means your AI client doesn't hallucinate; it grounds answers in actual API records, combining live store data with internal documents into one unified index.

Searching Customer and Product Data via LlamaIndex

Need to know why a customer complained about an order? You query the knowledge base, which contains indexed results from `get_zid_customer` and `list_zid_orders`. The system retrieves contextually relevant details rather than just providing raw API JSON. This mechanism turns structured e-commerce data into semantic search material. It’s huge for support agents who need quick answers grounded in history.

Managing Shop Metadata with LlamaIndex

The Zid MCP Server tools let you index shop profiles and status checks. By running `get_zid_shop_profile` or `check_zid_status`, you feed that metadata into your vector store. Later, you can ask complex questions like, 'What is the current operational status and payment method accepted?' This capability ensures all configuration data remains part of your searchable knowledge base.

Setup guide

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

You call `get_zid_customer` and then pass the resulting customer record into the indexing pipeline. This allows subsequent queries to semantically search not just by ID, but by attributes within that customer's history.

Absolutely. By running `list_zid_products`, the resulting product catalog data gets indexed. You can then query natural language questions like, 'Which products are currently out of stock?' and get an answer based on the API response.

You run `list_zid_orders` and include those results in your index. When you query, the system searches across all indexed order records simultaneously, giving a comprehensive view of fulfillment status.

You must run `list_zid_inventories` and include those results in your index before querying. The RAG process is only as current as the data you feed it.

You'll want to index the 'API Status' data. The `check_zid_status` tool provides a clean boolean or text result that can be easily searched and verified in your knowledge base.

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