How to Use the Mailingwork MCP in Pydantic AI

Build type-safe email automation by connecting Pydantic AI to Mailingwork with runtime validation.

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Connect Mailingwork MCP to Pydantic AI

Create your Vinkius account to connect Mailingwork 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 Mailingwork with Pydantic AI

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Type-safe subscriber operations in Pydantic AI

Every response from `get_subscriber` is automatically validated against your Pydantic models. If the Mailingwork API returns data that doesn't match your schema, the agent stops immediately. This prevents silent errors where your agent might otherwise hallucinate missing fields. You get a clear validation error, allowing you to debug the data structure before it impacts your workflow.

Reliable transactional email sending with Pydantic AI

Call `send_transactional_email` with confidence knowing the input is verified. Your agent's parameters are checked against your models, ensuring that required fields like recipient address are always present. This makes your email triggers predictable and stable. You don't have to worry about malformed requests reaching the Mailingwork servers.

Automated list management for Pydantic AI

Retrieve and filter your lists using `list_tags` and `list_subscribers`. Your agent can process these lists while strictly adhering to the types you have defined in your code. This keeps your agent's logic clean and focused. It eliminates the boilerplate code usually required to verify that the data you're receiving is actually what you expect.

Setup guide

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

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

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

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Common questions about Mailingwork MCP in Pydantic AI

You define your Pydantic models in your agent code. The toolset automatically validates the responses from the Mailingwork MCP Server against these definitions.

The agent will raise a validation error immediately. This stops the execution before any bad data can propagate through your system.

Yes, it supports both Streamable HTTP and SSE. You can choose the transport that provides the most reliability for your specific agent setup.

We use a zero-trust architecture that validates every action. Your subscriber emails and names are handled strictly within the ephemeral environment, minimizing exposure risk.

Yes, because the toolset is model-agnostic. Whether you use OpenAI, Anthropic, or a local model, the validation logic remains consistent.

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