How to Use the MJML (Email Markup) MCP in Pydantic AI

Q: How do I install MJML (Email Markup) for Pydantic AI?

Run pip install "pydantic-ai-slimmcp" to install the framework with MCP support. Then, initialize the connection using MCPToolset("http://...") and pass it to your agent's toolsets parameter.

Q: Can Pydantic AI validate the output of MJML (Email Markup)?

Yes. When your agent calls the rendermjml tool, Pydantic AI validates the returned HTML string against your defined schema. If the output is malformed or empty, a validation error is raised instantly.

Q: How does the agent handle rendering exceptions?

When the rendermjml tool fails due to invalid markup, it returns a clear error message. The framework captures this exception, allowing your agent to catch the error, log it, or attempt to self-correct the XML.

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Create your Vinkius account to connect MJML (Email Markup) 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 MJML (Email Markup) with Pydantic AI

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Validate email layouts at runtime using Pydantic AI

The `render_mjml` tool converts XML markup into responsive HTML code that is ready for production. Pydantic AI wraps this tool call, ensuring the returned HTML matches your expected schema and contains no broken structures. If the transpiler encounters syntax issues, the tool returns detailed error data. Pydantic AI's strict validation catches these issues immediately, failing loudly so you can fix errors before sending the email.

Ensure strict type safety for email generation

This MCP Server integrates with Pydantic AI's type-checked runtime to guarantee that your agent only passes valid XML or JSON to the rendering engine. The `render_mjml` tool expects a clean string, and the framework enforces this boundary before making the network call. This architecture prevents common LLM formatting errors, such as markdown code blocks leaking into the raw XML input. Your pipeline remains stable, and your templates compile without unexpected runtime crashes.

Run model-agnostic email rendering pipelines

This MCP Server provides model-agnostic email rendering across any LLM backend supported by Pydantic AI. The `render_mjml` tool performs the heavy lifting of HTML generation, keeping your agent logic portable. You configure the connection using the unified `MCPToolset` class, which handles the streamable HTTP transport. This setup lets you swap models behind the scenes without changing a single line of your email compilation code.

Setup guide

Set up MJML (Email Markup) 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": {
        "mjml-email-markup-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent MJML (Email Markup) 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 MJML. 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 MJML (Email Markup) MCP in Pydantic AI

Run `pip install "pydantic-ai-slim[mcp]"` to install the framework with MCP support. Then, initialize the connection using `MCPToolset("http://...")` and pass it to your agent's `toolsets` parameter.

Yes. When your agent calls the `render_mjml` tool, Pydantic AI validates the returned HTML string against your defined schema. If the output is malformed or empty, a validation error is raised instantly.

The MCP Server supports both Streamable HTTP and SSE transports. You connect your Pydantic AI agent to the externally running server using the unified `MCPToolset` class for reliable communication.

When the `render_mjml` tool fails due to invalid markup, it returns a clear error message. The framework captures this exception, allowing your agent to catch the error, log it, or attempt to self-correct the XML.

Your raw MJML XML data is processed in a secure, ephemeral V8 isolate managed by Vinkius. No data is written to disk or used for training, ensuring your proprietary templates and dynamic email copy remain completely private.

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