How to Use the AdRoll MCP in Pydantic AI

Enforce strict data validation for AdRoll using Pydantic AI and MCP.

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Connect AdRoll MCP to Pydantic AI

Create your Vinkius account to connect AdRoll 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 AdRoll with Pydantic AI

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Validate AdRoll campaign data in Pydantic AI

The server returns structured output that `pydantic-ai` validates against your local models. If `get_campaign_details` returns a malformed response, the agent flags it immediately. You catch schema mismatches before they break your logic. It ensures your agent only works with reliable, type-safe campaign info.

Fetch performance reports with Pydantic AI

Use `get_performance_report` to pull stats into your agent. Every field is checked against your Pydantic schemas, ensuring your numbers are always accurate. This approach eliminates the risk of silent data corruption. You get clear error messages if the API structure shifts or returns unexpected values.

List account assets for Pydantic AI

Call `list_advertisables` to bring your account list into your type-checked environment. The server output maps perfectly to your defined Python classes. This keeps your agent logic clean and predictable. You define the shape of your data, and the MCP Server fills it.

Setup guide

Set up AdRoll 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": {
        "adroll-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent AdRoll 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 AdRoll. 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 AdRoll MCP in Pydantic AI

Instantiate an MCPToolset with your server URL and pass it to your Agent. The framework validates every tool response against your Pydantic models automatically.

The agent will raise a validation error immediately. You get full transparency into why the data failed, preventing your logic from running on bad input.

Yes. This server is model-agnostic, so you can run it with local LLMs or cloud-based providers while keeping your strict type-checking in place.

Your data is scoped to your local agent instance. The Vinkius server acts as a clean conduit, and we don't store your campaign records.

No. The agent processes the performance records in memory and discards them once the task is complete. No persistent storage of your metrics occurs on our end.

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