How to Use the VBOUT MCP in Pydantic AI

Q: Can I find specific user information using VBOUT and Pydantic AI?

Yes. Use searchcontacts to filter for people quickly. Alternatively, running getcontact pulls the full record, with guaranteed data types you can trust.

Q: What happens when I trigger an automation using VBOUT and Pydantic AI?

The agent calls triggerworkflow, passing all needed inputs. Because of runtime validation, the system checks that these parameters are correct before attempting to run the workflow.

Q: Does this MCP Server support adding new leads through Pydantic AI?

Sure thing. addcontact requires an email and list ID. The framework validates these inputs, ensuring your new contact record is always correct in the database.

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Connect VBOUT MCP to Pydantic AI

Create your Vinkius account to connect VBOUT 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 VBOUT with Pydantic AI

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Maintain accurate contact data

The `add_contact` tool allows you to add a new person using just an email and list ID. The agent ensures this data conforms perfectly to your defined schema. Need existing info? Run `get_contact` or use `search_contacts` to find records, knowing every field returned is type-validated.

Process sales and content

The agent manages e-commerce transactions using `add_ecommerce_order`. This function validates all purchase data before it gets recorded. Similarly, `create_social_post` ensures the post text meets structural requirements. Keeping track of what's going on is easy; just call `list_campaigns` to see current initiatives.

Control automation flow

You can initiate complex actions by calling `trigger_workflow`, passing specific parameters. This tool requires a contact ID and executes the defined process. Before you start, check what's available using `list_workflows` or see which email lists are active with `list_email_lists`.

Setup guide

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

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

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

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Common questions about VBOUT MCP in Pydantic AI

The Pydantic AI framework validates every response from the MCP Server against your Python models. This means if a tool like `get_contact` returns bad data, your agent fails loudly—no silent errors.

Yes. Use `search_contacts` to filter for people quickly. Alternatively, running `get_contact` pulls the full record, with guaranteed data types you can trust.

The agent calls `trigger_workflow`, passing all needed inputs. Because of runtime validation, the system checks that these parameters are correct before attempting to run the workflow.

Sure thing. `add_contact` requires an email and list ID. The framework validates these inputs, ensuring your new contact record is always correct in the database.

This server touches contact records (email, name), e-commerce order details, and list membership. The primary benefit here is that all returned fields are strictly validated against your defined models.

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