How to Use the Adtelligent MCP in Pydantic AI

Q: How do I configure Pydantic AI for the Adtelligent integration?

Run pip install "pydantic-ai-slimmcp" first. Then instantiate MCPToolset with your Vinkius HTTP endpoint and pass it to your agent.

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Connect Adtelligent MCP to Pydantic AI

Create your Vinkius account to connect Adtelligent 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 Adtelligent with Pydantic AI

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Validate SSP statistics at runtime

Executing `get_ssp_report` fetches your yield metrics and forces them through strict schema checks. If the ad server returns a string instead of a float for revenue, the framework catches it. Failing loudly prevents corrupted data from entering your database. Your agent stops execution immediately rather than hallucinating a fix for the malformed performance report.

Sync DSP advertisers across any LLM

The `list_advertisers` tool works identically whether you run Anthropic, OpenAI, or a local model. Pydantic AI abstracts the function calling mechanics away from the specific provider. Swapping models requires zero changes to your integration logic. The agent continues pulling the demand-side partner list exactly as it did before the migration.

Load the Adtelligent MCP Server via HTTP

Hooking up `list_campaigns` uses the modern unified approach with `MCPToolset`. You pass the remote Vinkius endpoint, and the framework maps the DSP campaign structures automatically. Deprecated classes like `MCPServerHTTP` are no longer needed. The streamlined transport layer handles the connection, letting you focus on defining the exact Pydantic models for your media buys.

Setup guide

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

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

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

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Common questions about Adtelligent MCP in Pydantic AI

Run `pip install "pydantic-ai-slim[mcp]"` first. Then instantiate `MCPToolset` with your Vinkius HTTP endpoint and pass it to your agent.

The unified toolset handles both Streamable HTTP and Server-Sent Events. You pick the transport that best fits your external architecture.

The runtime throws a hard validation error. This design guarantees your application never processes silent corruptions or missing fields.

Vinkius manages the entire hosting infrastructure. Your code simply points to our managed URL to access the ad tech operations.

Your yield data and API credentials remain entirely separated from the LLM provider. The remote execution environment spins up, runs the authenticated request, and immediately terminates without logging the payload.

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