How to Use the Listrak MCP in Pydantic AI

Type-safe Listrak integration for Pydantic AI. Ensure every marketing action is validated at runtime.

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Connect Listrak MCP to Pydantic AI

Create your Vinkius account to connect Listrak 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 Listrak with Pydantic AI

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Type-safe messaging with Pydantic AI

Every response from `list_email_campaigns` or `get_email_contact_details` is validated against Pydantic models. You get a runtime error if the API returns unexpected types, preventing silent data corruption. Your agent won't guess the shape of the data. It enforces the contract, so your code remains predictable even when marketing APIs change their schema.

Validated SMS operations in Pydantic AI

Use `send_sms_broadcast` and `create_sms_contact` with the confidence that your inputs are strictly formatted. The agent verifies the data structure before the request hits the Listrak server. If the API returns a malformed response, Pydantic AI catches it immediately. You avoid the headache of debugging bad data deep in your message delivery pipeline.

Correctness-first marketing with Pydantic AI

Use `subscribe_to_sms_list` and `send_transactional_email` within your agent workflows. Because you are using a type-safe framework, every tool call is checked for consistency. This server provides the tools, but your Pydantic AI agent ensures the integrity of your marketing operations. It's the right choice for production systems where accuracy matters.

Setup guide

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

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

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

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Common questions about Listrak MCP in Pydantic AI

The agent throws a validation error immediately. You catch the issue at the boundary rather than letting bad data propagate through your system.

Yes. It uses the modern MCPToolset interface, making it easy to register all seven tools in a single line of code.

You can. The framework is model-agnostic, so you can run the agent locally against the Listrak server as long as your environment has the necessary access.

You define the expected schema in your Pydantic models. The agent ensures that the `messageId` and recipient details match your requirements before the tool executes.

Validation logic runs locally on your machine. We only interact with the API to perform the requested operations, ensuring your sensitive customer data isn't cached or leaked by the model.

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