How to Use the Adversus MCP in Pydantic AI

Use Pydantic AI with Adversus to enforce strict data validation on every sales automation response.

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Connect Adversus MCP to Pydantic AI

Create your Vinkius account to connect Adversus 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 Adversus with Pydantic AI

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Validate Adversus responses in Pydantic AI

Every tool call from `list_campaigns` is checked against Pydantic models at runtime. If the Adversus API returns unexpected data, the agent stops immediately. This prevents bad data from tricking your logic. You get a clean, typed interface for every piece of information your agent touches.

Execute campaign actions with Pydantic AI

The `add_contact_to_campaign` tool integrates directly into your agent's toolset. Pydantic AI ensures the contact data matches the expected schema before the server processes it. It turns your agent into a reliable engine for adding new prospects. You don't have to worry about malformed requests reaching your CRM.

Monitor team activity using Pydantic AI

Use `list_account_users` to pull team data into your agent while keeping your type definitions intact. Pydantic AI forces the agent to handle the user list as a structured array. This makes building complex multi-step workflows simple. Your agent understands the structure of your team data from the second it arrives.

Setup guide

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

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

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

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Common questions about Adversus MCP in Pydantic AI

You instantiate an MCPToolset with your server URL and include it in your agent definition. This automatically applies your Pydantic models to the incoming data.

Your agent will raise a validation error immediately. This protects your system from silent failures and bad data processing.

You can add as many tools as you need to the toolset. The agent decides which ones to use based on the task, and each one is validated individually.

The server provides raw data that the MCP client converts into models. It is fully compatible with the latest Pydantic AI slim package.

Your Vinkius endpoint token acts as the sole gatekeeper. Only your authenticated agent can request or modify lead records.

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