How to Use the AppLovin MCP in LlamaIndex

Q: How do I make AppLovin tools available to a LlamaIndex agent?

You'll use the McpToolSpec. Just pass it your MCP client instance. Then, call totoollistasync() to get a list of tools that you can pass directly to your LlamaIndex agent.

Q: Can I limit which AppLovin tools my LlamaIndex agent can use?

Absolutely. The McpToolSpec constructor accepts an allowedtools argument. You can pass it a list of tool names, like 'getmaxreport', 'getuseradrevenuereport', to restrict the agent's capabilities.

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Connect AppLovin MCP to LlamaIndex

Create your Vinkius account to connect AppLovin 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 AppLovin with LlamaIndex

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Index Your Ad Performance Data

Use tools like `get_max_report` and `get_user_ad_revenue_report` to fetch your latest ad performance metrics. LlamaIndex doesn't just show you the data; it indexes the results into a vector store. Now your raw data is a searchable asset. Schedule an agent to run these reports daily. Over time, you'll build a rich, historical knowledge base of your AppLovin performance. Ask questions in plain English and get answers grounded in your actual numbers, not hallucinations.

Build RAG on Live AppLovin Data

The `list_campaigns` and `get_app_discovery_report` tools let you pull live data about your user acquisition efforts. With LlamaIndex, you can combine this live API data with static documents—like marketing plans or quarterly goals—in a single index. This creates a powerful RAG system. When you ask "Which campaigns are underperforming against Q3 targets?", your agent can retrieve both the live performance data from the MCP tool and the targets from your documents to give a complete answer.

Query App Data with an MCP Tool Spec

The `list_apps` tool gives your agent a simple way to see all the applications in your account. You wrap the AppLovin tools in a `McpToolSpec` and LlamaIndex handles the rest. It makes the tools available to your agent for querying. You can filter which tools are available using `allowed_tools`. This lets you create specialized agents. For example, create a finance agent that can only access revenue reports like `get_max_cohort_report` and nothing else from this MCP Server.

Setup guide

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

You'll use the `McpToolSpec`. Just pass it your MCP client instance. Then, call `to_tool_list_async()` to get a list of tools that you can pass directly to your LlamaIndex agent.

Yes, that's the primary use case. By repeatedly calling tools like `get_max_report` and indexing the results, you build a vector index of your performance over time. Your agent can then query this index to identify trends.

This MCP Server standardizes the tool-use protocol. LlamaIndex's `McpToolSpec` knows how to speak MCP, so you don't have to write custom wrappers or parsers for the AppLovin API. It just works.

Absolutely. The `McpToolSpec` constructor accepts an `allowed_tools` argument. You can pass it a list of tool names, like `['get_max_report', 'get_user_ad_revenue_report']`, to restrict the agent's capabilities.

Your AppLovin performance data, like revenue and impression counts, is pulled through Vinkius's secure sandbox. You control where it gets indexed. Whether you use a local vector store or a cloud service, the security of the indexed data is up to you to manage. Vinkius never stores your data at rest.

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