How to Use the OpenPanel MCP in LlamaIndex

Q: How do I manage user identity across LlamaIndex query engines with OpenPanel?

You should call identifyuser at the beginning of the query lifecycle. This binds the user's vector search queries to their specific profile in OpenPanel. It ensures that all subsequent events tracked during the session are attributed to the correct user.

Q: How does LlamaIndex handle errors when tracking events?

If a call to trackevent or incrementproperty fails, the tool spec raises an error that your agent can handle gracefully. The agent can retry the call or log the failure in its session memory. This prevents tracking failures from crashing your entire query pipeline.

Index live OpenPanel analytics and user traits directly into your LlamaIndex knowledge graphs.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect OpenPanel MCP to LlamaIndex

Create your Vinkius account to connect OpenPanel to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

GDPR Included with Plan

Setup OpenPanel with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Ground LlamaIndex RAG queries in OpenPanel data

This MCP Server allows your LlamaIndex agent to query and update user metrics on the fly. By exposing tools like `identify_user` to your index, your agent can retrieve or set user profiles during a conversational query. The LlamaIndex agent can immediately log search behavior using `track_event` to build a rich history of what your users are looking for. You can index these OpenPanel search events back into your vector database to improve search relevance.

Track and index LlamaIndex metrics via MCP Server

Managing usage limits in a LlamaIndex RAG application is crucial for controlling API costs. Your LlamaIndex agent can run `increment_property` or `decrement_property` to update OpenPanel whenever a user runs a heavy vector search. Because the OpenPanel tool outputs are structured, LlamaIndex can index these property updates into your session memory. The LlamaIndex agent can check if a user has exceeded their limits before running a costly query.

Event-driven OpenPanel indexing pipelines

You can set up your LlamaIndex pipelines to trigger OpenPanel events based on document ingestion. When a new file is parsed and indexed, your LlamaIndex system can call `track_event` to log the document metadata. Using this MCP integration means you don't need to write custom OpenPanel tracking code inside your LlamaIndex data connectors. The LlamaIndex agent handles the telemetry naturally as part of its ingestion pipeline.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by OpenPanel. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect OpenPanel now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about OpenPanel MCP in LlamaIndex

While this MCP Server focuses on writing data using tools like `track_event`, you can index the resulting event payloads back into your vector store. This allows your agent to perform semantic searches over past user actions. It helps your agent understand the context of previous user sessions.

You should call `identify_user` at the beginning of the query lifecycle. This binds the user's vector search queries to their specific profile in OpenPanel. It ensures that all subsequent events tracked during the session are attributed to the correct user.

Yes, the server supports async execution, allowing your LlamaIndex agent to call `track_event` without blocking your document retrieval pipeline. This keeps your search latency low while ensuring your analytics are updated in real-time. It prevents tracking calls from slowing down your user experience.

If a call to `track_event` or `increment_property` fails, the tool spec raises an error that your agent can handle gracefully. The agent can retry the call or log the failure in its session memory. This prevents tracking failures from crashing your entire query pipeline.

Your user profiles and numeric properties are completely safe because this server only handles direct transmission to OpenPanel. It does not store your vectors or raw document text. All data is transmitted securely, ensuring that sensitive user metrics never sit in an unencrypted temporary database.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript