How to Use the Digital Turbine MCP in Pydantic AI

Use Pydantic AI and Digital Turbine to enforce strict data validation on every mobile campaign metric retrieved.

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Connect Digital Turbine MCP to Pydantic AI

Create your Vinkius account to connect Digital Turbine 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 Digital Turbine with Pydantic AI

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Enforce type-safe campaign tracking for Pydantic AI

Stop worrying about malformed API responses. Pydantic AI validates every output from `get_campaign_analytics` against your predefined Pydantic models at runtime. If the API returns unexpected fields, the agent stops immediately. This prevents bad data from polluting your growth models and ensures your agent only acts on verified information.

Validate monetization data in Pydantic AI

Use `get_app_monetization_details` to pull your app configuration with confidence. Pydantic AI checks the structure of the data before your agent makes any logic-based decisions. This approach removes the risk of hallucinated fields. You get a reliable bridge between your monetization settings and your agent’s decision-making process, ensuring correctness across your entire stack.

Audit performance data using Pydantic AI models

Your agent can pull `get_performance_summary` and have it instantly typed and checked. This makes it easy to monitor installs and CTR while ensuring the data matches your expected schema. It provides a robust way to handle API updates. When the Digital Turbine API changes, your Pydantic models catch the discrepancy immediately, preventing silent errors in your agent's reporting.

Setup guide

Set up Digital Turbine 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": {
        "digital-turbine-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Digital Turbine MCP in Pydantic AI

You import the MCPToolset and pass it to your agent. This setup allows for runtime validation of all tools, including those for campaign search and revenue reporting.

Yes. If the data from Digital Turbine does not match your Pydantic model, the agent will throw a validation error. This prevents your code from processing invalid metrics.

Yes, it supports both Streamable HTTP and SSE transports. You just need to point the MCPToolset to your running server instance.

Your data is secured through our V8 Isolate sandbox, which prevents unauthorized access to your monetization and campaign records. The Pydantic validation adds another layer of security by ensuring only expected data formats are processed.

The server exposes structured performance metrics, revenue reports, and app metadata. It does not touch or store any sensitive private data regarding your end users.

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