How to Use the Maropost MCP in Pydantic AI

Build type-safe marketing agents with Pydantic AI to validate Maropost contact and campaign data at runtime.

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Connect Maropost MCP to Pydantic AI

Create your Vinkius account to connect Maropost 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 Maropost with Pydantic AI

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Validate contact schemas in Pydantic AI

The `create_contact` tool writes subscriber data to your marketing lists with strict type validation. Pydantic AI ensures that every email, custom field, and tag matches your Python models before hitting the API. If the agent attempts to write a malformed phone number, the system throws a runtime error instead of sending bad data. You can use `get_contact` to pull existing profiles and validate them against the same strict models.

Parse campaigns using this MCP Server

The `get_campaign_details` tool fetches performance metrics that are immediately parsed into strongly-typed Python objects. Your agent reads open rates and click counts knowing that every float and integer is guaranteed correct. This strict typing applies when using `list_campaigns` to scan your entire marketing history. If Maropost modifies its payload structure, your code fails early and loudly, preventing corrupted reports.

Inspect Maropost structures with Pydantic AI

The `list_workflows` tool returns your active automation sequences as validated data structures. Your agent uses `get_workflow_details` to inspect step configurations, ensuring all branching paths match your internal schemas. This structured validation extends to list management via `list_lists` and `get_list_details`. Your application processes subscriber segments with total structural confidence, eliminating unexpected null values.

Setup guide

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

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

result = await agent.run("List recent Maropost 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 Maropost. 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 Maropost MCP in Pydantic AI

It prevents silent data corruption by validating all API payloads against Python models. Tools like `create_contact` and `list_campaigns` will never process malformed data, keeping your databases clean.

You pass the Vinkius HTTP endpoint to the MCPToolset constructor. This automatically registers tools like `list_lists` and `get_workflow_details` as validated MCP tools on your agent.

The framework raises a validation error immediately, stopping execution before the bad data propagates. This is critical when handling financial or subscriber records via `get_contact`.

Yes, the framework is model-agnostic. You can use local LLMs to run tools like `list_reports` and `get_report_details` without changing your validation logic.

All data transmission is encrypted in transit using standard TLS protocols directly to the Maropost API. The V8 sandbox environment of the MCP Server ensures that transient contact info and workflow details are discarded immediately after validation completes.

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