AppLovin MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add AppLovin as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
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 AppLovin. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in AppLovin?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About AppLovin MCP Server
The AppLovin MCP Server provides your AI agent with a powerful interface to your AppLovin and MAX mediation platforms. Gain instant insights into your monetization and user acquisition performance using simple natural language.
LlamaIndex agents combine AppLovin tool responses with indexed documents for comprehensive, grounded answers. Connect 7 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.
Key Features
- MAX Reporting — Access aggregated performance data for your MAX mediation, including revenue, impressions, and eCPM.
- User-Level Insights — Retrieve detailed revenue reports aggregated per user or per impression for granular analysis.
- Cohort Analytics — Monitor user retention and long-term value using MAX cohort reports.
- AppDiscovery Management — Track the performance of your UA campaigns and monitor growth trends.
- Campaign & App Inventory — List all active campaigns and tracked apps in your AppLovin account.
- Multi-Key Authentication — Securely uses both Report and Management keys to provide a comprehensive set of tools.
Benefits for Teams
- Ad Ops Managers — Quickly audit monetization performance and eCPM trends without manual dashboard exports.
- UA Specialists — Monitor campaign spend and performance across AppDiscovery using natural language.
- Growth Engineers — Analyze user-level revenue and cohort data to optimize long-term retention and ROI.
The AppLovin MCP Server exposes 7 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect AppLovin to LlamaIndex via MCP
Follow these steps to integrate the AppLovin MCP Server with LlamaIndex.
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 7 tools from AppLovin
Why Use LlamaIndex with the AppLovin MCP Server
LlamaIndex provides unique advantages when paired with AppLovin through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine AppLovin tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain AppLovin tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query AppLovin, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what AppLovin tools were called, what data was returned, and how it influenced the final answer
AppLovin + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the AppLovin MCP Server delivers measurable value.
Hybrid search: combine AppLovin real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query AppLovin 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 AppLovin for fresh data
Analytical workflows: chain AppLovin queries with LlamaIndex's data connectors to build multi-source analytical reports
AppLovin MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect AppLovin to LlamaIndex via MCP:
get_account_check
Verify AppLovin account connection
get_app_discovery_report
Get performance data for UA campaigns (AppDiscovery)
get_max_cohort_report
Get cohort analysis reports for MAX
get_max_report
Use columns, start, and end parameters. Get aggregated performance data for MAX mediation
get_user_ad_revenue_report
Get revenue data aggregated per user or per impression
list_apps
List apps tracked in your AppLovin account
list_campaigns
List UA campaigns from the management API
Example Prompts for AppLovin in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with AppLovin immediately.
"Show me the MAX revenue report for yesterday."
"List all active UA campaigns in AppLovin."
"Give me a cohort report for user retention from last month."
Troubleshooting AppLovin MCP Server with LlamaIndex
Common issues when connecting AppLovin to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpAppLovin + LlamaIndex FAQ
Common questions about integrating AppLovin MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect AppLovin with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect AppLovin to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.