How to Use the Freshmarketer MCP in Pydantic AI

Q: Can I use Pydantic AI to send transactional emails via Freshmarketer?

Yes. The sendtransactionalemail tool is available. Your agent will validate the payload structure against your Pydantic model before firing the email.

Ensure type-safe marketing automation with Pydantic AI and Freshmarketer for reliable, validated agent workflows.

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Connect Freshmarketer MCP to Pydantic AI

Create your Vinkius account to connect Freshmarketer 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 Freshmarketer with Pydantic AI

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Validate Freshmarketer actions in Pydantic AI

Every response from `list_contacts` gets checked against your models. If the API returns malformed data, your agent stops immediately. This prevents bad data from hitting your marketing lists. You get a clean interface for managing your contact database.

Trigger journeys with Pydantic AI

Your agent invokes `post_custom_event` to update customer journeys. Pydantic AI verifies the arguments before the request leaves your machine. This adds a layer of safety to your automated triggers. You know exactly what data is being sent to your marketing backend.

Manage lists using Freshmarketer MCP Server

Query your existing segments via `list_mailing_lists` to see where your leads belong. The agent parses the output into typed objects. It uses `create_mailing_list` to handle new segments on the fly. You gain programmatic control over your marketing structure.

Setup guide

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

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

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

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Common questions about Freshmarketer MCP in Pydantic AI

Install the MCP-enabled version of the library. Create an MCPToolset instance pointing to your Vinkius URL and pass it to your Agent definition.

It does. Since your tools are validated at runtime, a change in the API response will trigger a validation error, preventing silent failures.

Yes. The `send_transactional_email` tool is available. Your agent will validate the payload structure against your Pydantic model before firing the email.

The server is model-agnostic. You can use it with local models or cloud-based ones, provided the client library supports the MCP protocol.

The library enforces strict type checking on all inputs and outputs. This ensures that sensitive contact identifiers are not leaked into unexpected fields during processing.

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