OpenPanel MCP Server for LlamaIndexGive LlamaIndex instant access to 4 tools to Decrement Property, Identify User, Increment Property, and more

Name: OpenPanel
Price: 29 USD
Availability: InStock
Rating: 0.2 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add OpenPanel as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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The OpenPanel MCP Server for LlamaIndex is a standout in the Marketing Automation category — giving your AI agent 4 tools to work with, ready to go from day one.

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python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to OpenPanel. "
            "You have 4 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in OpenPanel?"
    )
    print(response)

asyncio.run(main())

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About OpenPanel MCP Server

Connect your OpenPanel account to any AI agent and manage your product analytics and user profiles through natural conversation.

LlamaIndex agents combine OpenPanel tool responses with indexed documents for comprehensive, grounded answers. Connect 4 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

Event Tracking — Record user actions and events with custom properties to understand application usage.
User Identification — Link events to specific individuals and maintain consistent user profiles across sessions.
Property Management — Dynamically increment or decrement numeric properties on user profiles (e.g., credits, points, or login counts).
Profile Enrichment — Set traits and custom metadata on user profiles to segment your audience effectively.

The OpenPanel MCP Server exposes 4 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 4 OpenPanel tools available for LlamaIndex

When LlamaIndex connects to OpenPanel through Vinkius, your AI agent gets direct access to every tool listed below — spanning event-tracking, user-profiles, product-analytics, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

decrement

Decrement property on OpenPanel

Decrement a numeric property on a user profile

identify

Identify user on OpenPanel

Identify a user in OpenPanel

increment

Increment property on OpenPanel

Increment a numeric property on a user profile

track

Track event on OpenPanel

Track an event in OpenPanel

Connect OpenPanel to LlamaIndex via MCP

Follow these steps to wire OpenPanel into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 4 tools from OpenPanel

Why Use LlamaIndex with the OpenPanel MCP Server

LlamaIndex provides unique advantages when paired with OpenPanel through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine OpenPanel tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain OpenPanel tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query OpenPanel, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what OpenPanel tools were called, what data was returned, and how it influenced the final answer

OpenPanel + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the OpenPanel MCP Server delivers measurable value.

Hybrid search: combine OpenPanel real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query OpenPanel to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying OpenPanel for fresh data

Analytical workflows: chain OpenPanel queries with LlamaIndex's data connectors to build multi-source analytical reports

Example Prompts for OpenPanel in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with OpenPanel immediately.

"Track a 'Sign Up' event for user 'user_99' with property 'source' as 'web'."

"Identify user 'alex_123' and set their email to 'alex@example.com'."

"Increment the 'credits' property by 50 for profile 'pro_user_01'."

Troubleshooting OpenPanel MCP Server with LlamaIndex

Common issues when connecting OpenPanel to LlamaIndex through Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

OpenPanel + LlamaIndex FAQ

Common questions about integrating OpenPanel MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query OpenPanel tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.