How to Use the MailerCheck MCP in Pydantic AI

Type-safe email verification for Pydantic AI using the MailerCheck MCP server.

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Connect MailerCheck MCP to Pydantic AI

Create your Vinkius account to connect MailerCheck 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 MailerCheck with Pydantic AI

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Type-safe single email verification

Call `verify_single_email` and let Pydantic AI validate the response against your defined models. If the API returns unexpected data, the agent fails immediately, preventing silent data errors. This gives you confidence in every verification result. You define the structure, and the framework enforces it at runtime.

Validated batch processing with Pydantic AI

Submit lists via `create_verification_batch` and handle the response objects with strict type checking. Your agent can safely process large batches knowing the structure matches your requirements. Use `get_batch_results` to retrieve the data once ready. The framework ensures that every field in the response is accounted for before your agent acts on it.

Account info with strict type enforcement

Use `get_account_info` to fetch your credit balance into a typed model. This helps your agent make decisions based on accurate, verified account data. If the data format changes, your agent catches the mismatch immediately. It’s the safest way to build reliable email automation.

Setup guide

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

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

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

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Common questions about MailerCheck MCP in Pydantic AI

You add the MCPToolset to your agent configuration. The framework will validate the tool outputs against the models you define for the server response.

Pydantic AI will raise a validation error. This prevents corrupted data from entering your system and forces the agent to handle the failure.

Yes, you define a model that represents the batch results. The agent uses the tool to get the data, and Pydantic AI checks the structure.

You do. The framework connects to the running server instance, and you manage that endpoint through the toolset configuration.

We use ephemeral, zero-trust infrastructure. Every email string is processed in an isolated sandbox and cleared immediately after the verification response is sent back to your agent.

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