How to Use the Zakeke MCP in LlamaIndex

Q: How can I query Zakeke orders using LlamaIndex?

Run getorderdetails to pull specific order records. By indexing this data, you create a searchable knowledge base for your RAG app. Users can then ask natural language questions and get answers grounded in the actual purchase details.

Q: Does LlamaIndex help with Zakeke design comparisons?

Yes. You index outputs from listdesigns and listcompositions. This allows you to ask complex, comparative questions—like 'Compare the specs of Design ABC versus Composition XYZ'—and get answers derived directly from the indexed API data.

Q: What information does Zakeke provide about print files in LlamaIndex?

The getdesignprintfiles tool gives you the ZIP output. When you index this, your knowledge base learns what these files contain and which designs they relate to. You can then query for printing requirements based on documented API data.

Q: If I need current product listings with Zakeke and LlamaIndex?

Use listproducts to get the latest catalog information. Passing this through LlamaIndex means that any future query about available products will be answered using the most up-to-date, indexed API facts.

Ground e-commerce answers and configurations with LlamaIndex's RAG over Zakeke data.

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Connect Zakeke MCP to LlamaIndex

Create your Vinkius account to connect Zakeke 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 Zakeke with LlamaIndex

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Querying complex product details

The `get_composition_details` tool retrieves specific 3D configuration specs. When you run this through LlamaIndex, the resulting structured data is indexed into your vector store. Now, you can query past sessions and get answers grounded in that API output. This means you don't just call a function; you build knowledge. Your RAG application combines live Zakeke API data with documents, giving accurate context to any user question.

Retrieving past customer designs

Need help finding an old project? Use `list_designs` to get a list of all saved user creations. By indexing the output of this tool, you can search across historical data semantically. You'll retrieve answers based on actual API records, not guesses. This is perfect for support agents or designers who need context from past interactions with your MCP Server.

Analyzing customized orders and carts

If a user asks about an order they placed, you can use `get_order_details` to fetch the data. LlamaIndex indexes this raw JSON output alongside documents. This allows your agent to answer questions like, 'What was ordered on May 1st?' based purely on indexed API facts. It's powerful because it merges the live e-commerce record with general knowledge in a unified index.

Setup guide

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

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Common questions about Zakeke MCP in LlamaIndex

Run `get_order_details` to pull specific order records. By indexing this data, you create a searchable knowledge base for your RAG app. Users can then ask natural language questions and get answers grounded in the actual purchase details.

Yes. You index outputs from `list_designs` and `list_compositions`. This allows you to ask complex, comparative questions—like 'Compare the specs of Design ABC versus Composition XYZ'—and get answers derived directly from the indexed API data.

The `get_design_print_files` tool gives you the ZIP output. When you index this, your knowledge base learns what these files contain and which designs they relate to. You can then query for printing requirements based on documented API data.

Use `list_products` to get the latest catalog information. Passing this through LlamaIndex means that any future query about available products will be answered using the most up-to-date, indexed API facts.

The server touches product availability and configuration. You can use `list_products` to get the master list, and then use `get_composition_details` to check if specific configurations are valid or active.

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