AppLovin MCP Server for Pydantic AI 7 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect AppLovin through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to AppLovin "
"(7 tools)."
),
)
result = await agent.run(
"What tools are available in AppLovin?"
)
print(result.data)
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.
Pydantic AI validates every AppLovin tool response against typed schemas, catching data inconsistencies at build time. Connect 7 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
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 Pydantic AI 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 Pydantic AI via MCP
Follow these steps to integrate the AppLovin MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
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 with type-safe schemas
Why Use Pydantic AI with the AppLovin MCP Server
Pydantic AI provides unique advantages when paired with AppLovin through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your AppLovin integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your AppLovin connection logic from agent behavior for testable, maintainable code
AppLovin + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the AppLovin MCP Server delivers measurable value.
Type-safe data pipelines: query AppLovin with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple AppLovin tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query AppLovin and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock AppLovin responses and write comprehensive agent tests
AppLovin MCP Tools for Pydantic AI (7)
These 7 tools become available when you connect AppLovin to Pydantic AI 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 Pydantic AI
Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI
Common issues when connecting AppLovin to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiAppLovin + Pydantic AI FAQ
Common questions about integrating AppLovin MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
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 Pydantic AI
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.