How to Use the MJML (Email Markup) MCP in LangChain

Q: What happens if my agent generates invalid markup?

The rendermjml tool returns clear validation errors to the agent. The LangChain agent can read these errors, fix the syntax, and retry the compilation automatically.

Get responsive, table-heavy HTML emails compiled directly within your LangChain reasoning loops.

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Connect MJML (Email Markup) MCP to LangChain

Create your Vinkius account to connect MJML (Email Markup) to LangChain 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 LangChain

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Dynamic email rendering in LangChain chains

Your LangChain agent can now build email layouts dynamically. Instead of guessing how Outlook will mangle your custom CSS, the agent passes raw template strings to the `render_mjml` tool. The MCP server runs the compiler and returns clean, responsive HTML instantly. This fits perfectly into multi-step chains. The output of your content-generation step feeds directly into the renderer, letting your agent inspect and tweak the layout before final delivery.

Trace rendering performance with LangSmith

Keep an eye on latency and token usage during compilation. Every call to the `render_mjml` tool is fully traceable using LangSmith, showing you exactly how fast your templates are rendering. If a complex layout slows down your agent, you will see it in the trace. This helps you optimize your templates before deploying them to your production pipeline.

Multi-server setups with this MCP Server

Combine email compilation with database queries or external APIs. By registering this MCP Server alongside your other tools, your LangChain agent can fetch customer data and inject it straight into the email template. The agent handles the orchestration, deciding when to fetch data and when to trigger the `render_mjml` tool. You get a unified interface without writing custom glue code for every service.

Setup guide

Set up MJML (Email Markup) MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes MJML (Email Markup) tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "mjml-email-markup-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent MJML (Email Markup) transactions"
    })
    print(result["messages"][-1].content)

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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 LangChain

Install the adapter package first. Use the MultiServerMCPClient to point to the Vinkius endpoint, retrieve the tools with client.get_tools(), and pass them to your agent.

Yes. Your agent can construct highly nested templates and pass them to render_mjml in a single tool call. The server processes the entire payload and returns the final HTML.

No, the rendering is fast enough to run inline. The server compiles the markup in milliseconds, keeping your agent's reasoning loop moving without lag.

The render_mjml tool returns clear validation errors to the agent. The LangChain agent can read these errors, fix the syntax, and retry the compilation automatically.

Yes. Your template strings and generated HTML are processed in memory and never stored, keeping your proprietary layouts private.

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