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

Q: Can I store the output of MJML (Email Markup) in my LlamaIndex vector store?

Yes. You can take the HTML returned by rendermjml and index it directly into your vector database. This makes your past email layouts searchable for future generation.

Index your email layouts and compile responsive HTML directly from your LlamaIndex RAG pipeline.

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Connect MJML (Email Markup) MCP to LlamaIndex

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

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Ground email generation in live index data

Stop guessing what content belongs in your newsletters. Your LlamaIndex agent queries your vector stores, pulls relevant context, and formats it into clean markup before calling `render_mjml` through the MCP server. The resulting HTML is perfectly formatted for all devices. By combining search retrieval with live compilation, you ensure your emails are both accurate and responsive.

Index compiled HTML for semantic search

Our MCP Server lets LlamaIndex index the outputs of the `render_mjml` tool. This lets you build a searchable archive of sent emails or successful layouts. When your agent needs to generate a new campaign, it can search past successful templates to see what worked, maintaining design consistency across runs.

Structured data access via this MCP Server

Use `McpToolSpec` to load the tool into your index. Your agent can then decide when to call the compilation tool based on the user's query. This turns your email template engine into a queryable resource. The agent treats the compilation tool as just another data source in your RAG setup.

Setup guide

Set up MJML (Email Markup) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all MJML (Email Markup) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to MJML (Email Markup) tools.",
)
response = await agent.run("List recent MJML (Email Markup) data")

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Common questions about MJML (Email Markup) MCP in LlamaIndex

You initialize the tool using llama-index-tools-mcp. The McpToolSpec exposes the render_mjml tool directly to your index agents for immediate use.

Yes. You can take the HTML returned by render_mjml and index it directly into your vector database. This makes your past email layouts searchable for future generation.

If the markup is malformed, the tool returns the error details. Your LlamaIndex agent can use this feedback to correct the template structure before trying again.

Yes. Your agent can fetch raw layout snippets from your vector store, assemble them, and pass the final string to render_mjml for compilation.

All transpilation occurs within ephemeral, zero-trust sandboxes. Your raw markup and compiled HTML never touch persistent storage, ensuring your customer templates remain private.

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