How to Use the Zinrelo MCP in LlamaIndex

Q: How do I search Zinrelo's transaction history using LlamaIndex?

You call listmembertransactions via the MCP tool, and then index that output. You can query the knowledge base later to find specific transactions without re-running the API call.

Q: Can I use Zinrelo's reward redemption data with LlamaIndex?

Yes. By indexing the results of redeemreward, you can build a knowledge graph that answers questions about past redemptions and available rewards, improving context for your AI client.

Q: Does the Zinrelo MCP Server support member enrollment with LlamaIndex?

The enrollmember tool is fully supported. You can capture the output of this call and add it to your indexed knowledge base for future reference and querying.

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Connect Zinrelo MCP to LlamaIndex

Create your Vinkius account to connect Zinrelo 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 Zinrelo with LlamaIndex

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Indexing Loyalty Program Data

LlamaIndex allows you to index the live output of your MCP tools. After calling `list_member_transactions`, that entire JSON payload becomes a searchable document in your vector store. You can then query past API data—like specific redemption details from `redeem_reward`—and get answers grounded directly in Zinrelo's records, eliminating hallucinations.

Semantic Search on Member Records

Instead of just retrieving a member list with `list_loyalty_members`, you index those results. You can then ask a natural language question—like 'Show me all members who bought X last month'—and LlamaIndex finds the relevant data points across your knowledge base. This turns raw API calls into actionable, searchable intelligence for your application.

Building RAG on Point Activity

The system makes it easy to combine documents and live data. For example, you can index the rules from `get_loyalty_settings` alongside internal policy documents. Your agent uses this combined knowledge base to answer complex questions about point eligibility. This is perfect for creating unified indexes that cover both written policies and real-time customer activity.

Setup guide

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

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Common questions about Zinrelo MCP in LlamaIndex

You call `list_member_transactions` via the MCP tool, and then index that output. You can query the knowledge base later to find specific transactions without re-running the API call.

The server provides structured customer loyalty information, including points awarded via `award_points_activity` and member details. All of this output is designed to be indexed for semantic search.

Yes. By indexing the results of `redeem_reward`, you can build a knowledge graph that answers questions about past redemptions and available rewards, improving context for your AI client.

The `enroll_member` tool is fully supported. You can capture the output of this call and add it to your indexed knowledge base for future reference and querying.

The primary data type is customer loyalty records. This includes point balances, membership status, and transaction history associated with specific members.

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