How to Use the Leal MCP in LlamaIndex

Q: Can LlamaIndex query Leal member profiles?

It can. Once you fetch the profile via getmember, the data becomes an entry in your knowledge graph or vector store.

Index your Leal loyalty data into LlamaIndex. Build RAG apps that ground answers in live customer transaction history.

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Connect Leal MCP to LlamaIndex

Create your Vinkius account to connect Leal 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 Leal with LlamaIndex

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Ground LlamaIndex queries in Leal data

Pull `get_member_transactions` output directly into your vector store. Your index now contains actual purchase history, allowing you to answer questions about customer behavior with high precision. This approach eliminates hallucinations. When you query your index, LlamaIndex retrieves the raw transaction data provided by the MCP server to build its response.

Searchable retail rewards with MCP Server

Use `list_rewards` to populate your knowledge base with current offers. Your agent can then perform semantic searches to find the best reward for a specific user segment. This keeps your knowledge base fresh. As soon as a reward is updated in the backend, your index reflects the change during the next retrieval cycle.

Index loyalty segments with MCP

Retrieve `list_segments` and `get_segment` details to categorize your user base within your vector store. LlamaIndex uses these segments to provide context-aware answers to complex queries. Your agent now understands the relationship between specific segments and their spending habits. It uses this indexed information to tailor its output to the right audience.

Setup guide

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

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Common questions about Leal MCP in LlamaIndex

You use the BasicMCPClient to pull data via the tools. Then, you pass those tool outputs into your document indexer for searchability.

It can. Once you fetch the profile via `get_member`, the data becomes an entry in your knowledge graph or vector store.

We keep your credentials isolated on the Vinkius platform. Only the authorized agent can interact with your member's private transaction records.

The tools fetch live data from the API every time your agent triggers them. This ensures your index stays updated with the latest activity.

The server shares member profiles, point balances, and transaction logs. This information is strictly used to build your local search index.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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