How to Use the Luma MCP in LlamaIndex

Turn your Luma event data into a searchable knowledge base for your LlamaIndex RAG applications.

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Connect Luma MCP to LlamaIndex

Create your Vinkius account to connect Luma 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 Luma with LlamaIndex

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Index event data for semantic retrieval

LlamaIndex takes the output from `list_events` and turns it into a searchable index. Your agent no longer guesses about past gatherings; it queries the vector store to find exact event details. This makes your RAG application smarter by grounding answers in your actual history. It uses `list_organization_events` to pull in broader context when needed.

Query guest lists with natural language

Instead of parsing raw JSON, you ask your agent about your attendees. It calls `list_event_guests` and indexes the returned records so you can ask questions like who attended a specific session. This turns your guest list into a dynamic resource. The agent retrieves the necessary details and synthesizes them into a readable summary for you.

Automate event infrastructure via LlamaIndex

You can trigger `create_event` directly from your agent's reasoning process. It maps your natural language requirements to the tool parameters, ensuring events are created exactly as you requested. This removes the need for manual data entry. The agent handles the tool call and indexes the confirmation, keeping your local knowledge base up to date with the latest API responses.

Setup guide

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

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Common questions about Luma MCP in LlamaIndex

You use the MCP tool spec to convert Luma tools into a format LlamaIndex understands. The agent then performs the calls and indexes the results automatically.

Yes, by indexing the output of the list tools, your agent can perform semantic searches across your entire event history. It provides answers based on the actual records.

You should enable all six tools to get the full benefit. Specifically, the list tools are crucial for building an accurate index of your events and subscribers.

The MCP client supports asynchronous calls, so you can fetch large guest lists or organization-wide events without blocking your main application loop.

Subscriber records are retrieved only when requested by your agent. The data remains scoped to your local index, ensuring sensitive contact lists aren't exposed to external models.

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