How to Use the Eventee MCP in LlamaIndex

Q: How do I load Eventee tools into LlamaIndex?

Install llama-index-tools-mcp and set up a BasicMCPClient. Wrap it with McpToolSpec and call await mcptoolspec.totoollistasync() to get the functions.

Q: Can LlamaIndex filter which Eventee tools the agent uses?

You can restrict the agent's access. Use the allowedtools parameter to give it only listcurrentlyactiveevents if you want to limit its scope to live data.

Q: How does LlamaIndex handle the Eventee engagement audit?

The agent calls quickeventengagementaudit and indexes the summary metrics. You can then query those stats against historical event data stored in your vector database.

Q: Are my Eventee speaker and sponsor lists secure in LlamaIndex?

The MCP protocol operates on a zero-trust model. When listeventspeakers runs, the data routes through an ephemeral instance that tears down instantly. No speaker bios or contact details linger in the server memory.

Index live Eventee schedules, speaker lists, and attendee data directly into your LlamaIndex RAG applications.

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Connect Eventee MCP to LlamaIndex

Create your Vinkius account to connect Eventee 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 Eventee with LlamaIndex

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Index Eventee MCP Server Output

`get_event_detailed_data` pulls the core configuration for your event so LlamaIndex can embed it into a vector store. Instead of just reading the API, your application converts the event settings into a searchable knowledge base. You ask a question, and the agent retrieves the exact configuration details. You combine this with live updates. Running `list_event_sessions` fetches the schedule, which LlamaIndex indexes alongside your static documents. When someone asks what time a keynote starts, the answer comes from actual API data, not a hallucination.

Semantic Search for Event Questions

`list_event_audience_questions` grabs all the Q&A submissions from your live audience. LlamaIndex takes those raw strings and turns them into embeddings. Your RAG setup can now group similar questions together semantically. This changes how you handle post-event analysis. You query the index for specific themes, and the agent pulls up the exact questions attendees asked. It grounds the response in the actual text pulled from the event.

Grounded Speaker and Sponsor Queries

`list_event_speakers` feeds the speaker roster straight into your index. If you need to know who is presenting on a specific topic, the agent searches the vector store instead of scanning a massive JSON payload. It is fast and precise. The same applies to sponsors. `list_event_partners_and_sponsors` adds your partner list to the knowledge base. You can build internal tools that let your team query sponsor commitments using natural language.

Setup guide

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

Install `llama-index-tools-mcp` and set up a `BasicMCPClient`. Wrap it with `McpToolSpec` and call `await mcp_tool_spec.to_tool_list_async()` to get the functions.

You can restrict the agent's access. Use the `allowed_tools` parameter to give it only `list_currently_active_events` if you want to limit its scope to live data.

The agent calls `quick_event_engagement_audit` and indexes the summary metrics. You can then query those stats against historical event data stored in your vector database.

No. LlamaIndex handles the schema conversion natively. The tool outputs are automatically formatted so the embedding model can process them.

The MCP protocol operates on a zero-trust model. When `list_event_speakers` runs, the data routes through an ephemeral instance that tears down instantly. No speaker bios or contact details linger in the server memory.

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