AppLovin MCP Server for LangChain 7 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect AppLovin through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"applovin": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using AppLovin, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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.
LangChain's ecosystem of 500+ components combines seamlessly with AppLovin through native MCP adapters. Connect 7 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain via MCP
Follow these steps to integrate the AppLovin MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 7 tools from AppLovin via MCP
Why Use LangChain with the AppLovin MCP Server
LangChain provides unique advantages when paired with AppLovin through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine AppLovin MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across AppLovin queries for multi-turn workflows
AppLovin + LangChain Use Cases
Practical scenarios where LangChain combined with the AppLovin MCP Server delivers measurable value.
RAG with live data: combine AppLovin tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query AppLovin, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain AppLovin tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every AppLovin tool call, measure latency, and optimize your agent's performance
AppLovin MCP Tools for LangChain (7)
These 7 tools become available when you connect AppLovin to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting AppLovin to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersAppLovin + LangChain FAQ
Common questions about integrating AppLovin MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
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 LangChain
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.