How to Use the AppLovin MCP in OpenAI Agents SDK

Q: How do I connect AppLovin to the OpenAI Agents SDK?

Install openai-agents via pip. Instantiate MCPServerStreamableHttp with your Vinkius endpoint token, then pass it in the mcpservers list when creating your Agent.

Q: Does OpenAI Agents SDK support AppLovin cohort analysis?

It does. Your agent can execute the getmaxcohortreport tool to pull retention and LTV data straight from the MAX platform.

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Connect AppLovin MCP to OpenAI Agents SDK

Create your Vinkius account to connect AppLovin to OpenAI Agents SDK 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 OpenAI Agents SDK

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Pull MAX revenue data into OpenAI Agents SDK

The `get_max_report` tool gives your deployed agent direct access to aggregated MAX mediation performance using start and end parameters. You configure the guardrails in Python, defining exactly which columns the model can pull from the AppLovin MCP Server. Handoffs between specialized agents work perfectly here. One sub-agent runs `get_max_cohort_report` to analyze user retention, while another checks `get_app_discovery_report` for UA spend. The OpenAI dashboard traces every API call your system makes so you can audit the exact data flow.

Track user-level ad revenue safely

Calling `get_user_ad_revenue_report` exposes impression-level monetization data directly to your Python application. The SDK validates the agent's intent before executing the request, preventing unauthorized data access. Instead of building custom ETL pipelines, your system queries user revenue on demand. Pass the AppLovin MCP server to the Agent constructor with `cacheToolsList=True` so your production environment avoids constant tool re-discovery.

Audit tracked apps and UA campaigns

Your agent uses `list_apps` and `list_campaigns` to map out the current state of your AppLovin management API. The OpenAI framework ensures the LLM sticks to reading these lists without hallucinating non-existent app IDs. Start by running `get_account_check` to verify the connection. Once authenticated, your multi-agent setup can map specific User Acquisition campaigns to their corresponding AppDiscovery performance metrics, all tracked safely in your production logs.

Setup guide

Set up AppLovin MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all AppLovin tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives AppLovin tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate AppLovin tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="AppLovin Agent",
            instructions="You have access to AppLovin tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about AppLovin MCP in OpenAI Agents SDK

Install `openai-agents` via pip. Instantiate `MCPServerStreamableHttp` with your Vinkius endpoint token, then pass it in the `mcp_servers` list when creating your Agent.

Yes. The framework auto-discovers all seven available tools like `get_max_report` when you connect the server. Set `cacheToolsList=True` to speed up initialization.

It does. Your agent can execute the `get_max_cohort_report` tool to pull retention and LTV data straight from the MAX platform.

You build specialized agents for each network. The framework handles the handoffs, routing AppDiscovery queries to this specific MCP Server while other agents interact with their respective APIs.

Your impression-level revenue and UA campaign metrics remain encrypted in transit. Vinkius runs the integration in a V8 Isolate Sandbox, destroying the ephemeral environment the moment your Python script finishes execution.

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