How to Use the AppLovin MCP in Pydantic AI

Q: How do I configure AppLovin with Pydantic AI?

Install the slim package with MCP support. Create an MCPToolset pointing to your Vinkius HTTP endpoint, then pass it in the toolsets array to your Agent.

Q: Does Pydantic AI validate the AppLovin report columns?

Yes. When the agent calls getmaxreport, the returned tabular data must match the Pydantic models you define. Any deviation triggers a strict runtime error.

Q: Can I use local models to query AppLovin data in Pydantic AI?

Pydantic AI is completely model-agnostic. You can route getappdiscoveryreport queries through OpenAI, Anthropic, or a locally hosted model while maintaining identical type safety.

Q: Which transport method should I use for this server?

Use Streamable HTTP or SSE transports. The older MCPServerHTTP class is deprecated, so stick to the modern MCPToolset implementation for external MCP Server connections.

Enforce strict type validation on AppLovin MAX reports using Pydantic AI.

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Connect AppLovin MCP to Pydantic AI

Create your Vinkius account to connect AppLovin to Pydantic AI 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 Pydantic AI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Type-safe MAX mediation reporting

The `get_max_report` tool fetches your aggregated advertising performance while your Pydantic AI agent validates every returned column. If the API returns a string where a float is expected, the framework throws a validation error immediately. You connect the MCP Server using the unified `MCPToolset` approach. The agent passes the start and end parameters, and the response maps perfectly to your predefined Python models without silent data corruption.

Validate user-level revenue and cohorts

Running `get_user_ad_revenue_report` guarantees your impression data matches your exact schema requirements. Your system uses `get_max_cohort_report` alongside it, failing loudly if the cohort analysis structure changes unexpectedly. This strict runtime validation works with any LLM you choose. You can back the agent with Claude, Gemini, or a local model. The AppLovin data strictly adheres to the types you define in your application logic.

Audit AppLovin MCP Server campaigns

Your agent pulls your active portfolio via `list_apps` and `list_campaigns` from the management API. The Pydantic framework ensures the returned app IDs and UA campaign names are properly formatted before your code processes them. Verify your credentials upfront by triggering `get_account_check`. After confirming the connection, your agent can safely request `get_app_discovery_report` to analyze UA performance, knowing the output will match your data models perfectly.

Setup guide

Set up AppLovin MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "applovin-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to AppLovin tools.",
)

result = await agent.run("List recent AppLovin transactions")
print(result.output)

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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 Pydantic AI

Install the slim package with MCP support. Create an `MCPToolset` pointing to your Vinkius HTTP endpoint, then pass it in the `toolsets` array to your Agent.

Yes. When the agent calls `get_max_report`, the returned tabular data must match the Pydantic models you define. Any deviation triggers a strict runtime error.

Pydantic AI is completely model-agnostic. You can route `get_app_discovery_report` queries through OpenAI, Anthropic, or a locally hosted model while maintaining identical type safety.

Use Streamable HTTP or SSE transports. The older `MCPServerHTTP` class is deprecated, so stick to the modern `MCPToolset` implementation for external MCP Server connections.

Your raw user ad revenue and cohort metrics traverse a strictly ephemeral connection. Vinkius requires only a single endpoint token to authenticate the zero-trust sandbox, leaving zero residual data after the request completes.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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