How to Use the AdButler MCP in Pydantic AI

Q: How do I initialize AdButler with Pydantic AI?

Install the slim package with the MCP extra. Create an MCPToolset using your Vinkius HTTP endpoint, then attach it to your agent via the toolsets parameter.

Q: How does Pydantic AI fetch AdButler publisher zones?

The agent executes listpublishers to gather valid IDs. It then passes those typed IDs into listzones to pull the active ad placements.

Build type-safe ad agents by connecting AdButler to Pydantic AI with strict runtime validation.

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Connect AdButler MCP to Pydantic AI

Create your Vinkius account to connect AdButler 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 AdButler with Pydantic AI

Ask AI about this MCP

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Claude

Perplexity

Validated reporting data

Extracting campaign stats depends on the `get_performance_report` tool to fetch impressions, clicks, and CTR. Pydantic AI forces every returned metric to match your predefined schemas at runtime. Unexpected data structures trigger an immediate, loud validation error. You never have to worry about silent corruption or hallucinated numbers polluting your downstream financial models.

AdButler MCP Server auditing

Reviewing active advertisements requires the `list_campaigns` and `list_creatives` tools to retrieve pending self-serve records and banner assets. Your agent reads the exact library state directly from the source. Because this framework is model-agnostic, you can swap between models without changing the tool logic. The `MCPToolset` handles the unified connection to the external MCP server running on Vinkius.

Strict inventory mapping

Validating ad placements uses the `list_publishers` and `list_zones` tools to confirm which inventory belongs to which manager. Passing a Publisher ID yields a strict list of active ad zones. Correctness matters more than speed when dealing with ad inventory. The system validates the publisher IDs before attempting to fetch zones, preventing wasted API calls and broken execution loops.

Setup guide

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

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

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

Install the slim package with the MCP extra. Create an `MCPToolset` using your Vinkius HTTP endpoint, then attach it to your agent via the `toolsets` parameter.

Yes. If the server returns a campaign missing a required field, the framework throws a validation error immediately. Your agent stops before processing bad data.

You can. The framework abstracts the model layer completely. Your local LLM invokes the exact same tools as Anthropic or Gemini.

The agent executes `list_publishers` to gather valid IDs. It then passes those typed IDs into `list_zones` to pull the active ad placements.

Your self-serve campaign names and statuses flow through an ephemeral V8 isolate. Vinkius destroys the sandbox the millisecond the request finishes. No persistent storage exists between your agent's calls.

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