How to Use the MailWizz MCP in Pydantic AI

Q: How do I hook up MailWizz to Pydantic AI?

Install pydantic-ai-slimmcp. Create an MCPToolset pointing to your MailWizz MCP Server HTTP endpoint, then pass it to your agent's toolsets parameter.

Q: How do I check campaign stats with this integration?

Your agent calls getcampaigndetails with the campaign UID. The response is parsed into your Pydantic model, guaranteeing you get the exact metrics you expect.

Bring strict runtime validation to the MailWizz MCP Server with Pydantic AI so your agents never hallucinate a subscriber update.

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Connect MailWizz MCP to Pydantic AI

Create your Vinkius account to connect MailWizz 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 MailWizz with Pydantic AI

Ask AI about this MCP

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Claude

Perplexity

Type-safe subscriber handling

The `update_list_subscriber` tool modifies contact records with absolute strictness. Pydantic AI validates every field your agent tries to send against your predefined models. If the agent invents a custom field that doesn't exist, the framework throws a loud validation error. It uses `add_subscriber_to_list` for new opt-ins under the same rigid rules. You get guaranteed data shape before the API request ever leaves your server. No silent failures. No corrupted lists.

MailWizz MCP Server campaign reads

The `get_campaign_details` tool fetches the exact configuration of a specific send. Your agent reads the subject line, sender details, and tracking settings to ensure compliance before anything goes out. If you need a broader view, the agent calls `list_email_campaigns` to pull all active blasts. Because Pydantic AI is model-agnostic, you can use a cheap local model to parse this data or send it to Claude for deep analysis.

List auditing and cleanup

The MCP server's `list_list_subscribers` tool pulls your current contacts so the agent can audit them against your internal database. The agent checks for discrepancies in subscription status. When it finds a mismatch, it fires `delete_list_subscriber` to remove the outdated record. Everything happens synchronously. You know exactly what the agent did because the tool calls are fully typed.

Setup guide

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

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by MailWizz. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about MailWizz MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]`. Create an `MCPToolset` pointing to your MailWizz MCP Server HTTP endpoint, then pass it to your agent's `toolsets` parameter.

Yes. The framework enforces strict parameter validation. If the agent tries to pass a malformed list UID to `delete_list_subscriber`, Pydantic AI blocks the execution and throws a runtime error.

It does. Since the framework is model-agnostic, you can route `list_subscriber_collections` data to a local Llama 3 instance just as easily as GPT-4.

Your agent calls `get_campaign_details` with the campaign UID. The response is parsed into your Pydantic model, guaranteeing you get the exact metrics you expect.

Vinkius processes your subscriber emails and custom field data inside a strict V8 Isolate sandbox. The container is completely ephemeral, meaning your PII is wiped from memory the instant the MailWizz API responds.

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