How to Use the Cakemail MCP in Pydantic AI

Use Pydantic AI to build type-safe Cakemail automations that fail fast if data is malformed.

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Connect Cakemail MCP to Pydantic AI

Create your Vinkius account to connect Cakemail 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 Cakemail with Pydantic AI

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Type-safe Cakemail control for Pydantic AI

Every response from `get_campaign` gets checked against Pydantic models at runtime. If the API returns garbage, your agent stops immediately to prevent errors. You call `create_campaign` with full confidence that your data structures are correct. It prevents silent failures in your production pipelines.

Manage contact lists in Pydantic AI

The agent uses `create_contact` to update your mailing lists while ensuring all fields match your schema. It forces strict adherence to your data requirements. Executing `list_contact_lists` allows the agent to choose the right destination for new leads. Any unexpected API behavior triggers an immediate validation error.

Audit Cakemail analytics through MCP Server

Your agent runs `get_analytics` to pull logs that the Pydantic models validate instantly. You get clean, typed data that your agent can actually trust. Using `get_account_info` ensures your agent understands the current state of your setup. It keeps your implementation predictable and stable.

Setup guide

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

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

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

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Common questions about Cakemail MCP in Pydantic AI

You install the slim version of the package and use the MCPToolset class. Pass the server URL directly to initialize your agent's capabilities.

You get runtime validation for every response from the server. If the API returns an unexpected field, the agent fails loudly rather than hallucinating data.

Yes, it supports both Streamable HTTP and SSE. You just need to point the toolset to the correct endpoint.

The framework is model-agnostic, so you can connect this server to any local model you prefer. The validation logic remains identical regardless of the underlying LLM.

The server runs in a zero-trust, ephemeral sandbox managed by Vinkius. Your API keys are encrypted at rest and never shared with the model provider.

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