Postmark MCP Server

Building a Postmark MCP Server: Simplifying Email Integration with Model Context Protocol

Email remains one of the most effective channels for user communication, but integrating email services into your AI applications can be challenging. In this post, I’ll walk through how I built a Model Context Protocol (MCP) server for Postmark, a reliable transactional email service, making it easier to send emails through AI assistants and other MCP-compatible applications.

What is Model Context Protocol (MCP)?

The Model Context Protocol is a standardized way for AI models to interact with external tools and services. Think of it as a bridge that allows AI systems to seamlessly connect with real-world applications and data sources. By implementing an MCP server, we can expose functionality to AI assistants that can then execute actions on our behalf.

Why Postmark?

Postmark offers a robust, developer-friendly email delivery service with excellent deliverability rates. It’s particularly well-suited for transactional emails where reliability matters. By wrapping Postmark’s functionality in an MCP server, we create a simple, standardized way for AI systems to send emails without needing to understand Postmark’s specific API details.

Project Overview

The Postmark MCP Server is a lightweight Node.js application that:

Exposes a standardized MCP interface for sending emails
Handles the authentication and connection to Postmark’s API
Provides a simple configuration through environment variables
Operates with minimal setup requirements

Setting Up the Project

Let’s start by setting up the basic project structure:

# Clone the repository
git clone https://github.com/jaballer/postmark-mcp-server.git
cd postmark-mcp-server

# Install dependencies
npm install

# Configure environment variables
cp .env.example .env
# Edit .env with your Postmark credentials

The .env file needs three key configuration values:

POSTMARK_SERVER_TOKEN=your-postmark-server-token
DEFAULT_SENDER_EMAIL=info@example.com
DEFAULT_MESSAGE_STREAM=outbound

The Core Implementation

The heart of our MCP server is the postmark-mcp-server.js file, which:

Sets up the MCP server
Configures the Postmark client
Registers the sendEmail tool
Handles the communication protocol

Let’s break down the key parts of the implementation:

Setting Up Dependencies

import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";
import postmark from "postmark";

We’re using:

The MCP SDK to create our server
Zod for schema validation
Postmark’s Node.js client for email sending

Configuring Postmark

const serverToken = process.env.POSTMARK_SERVER_TOKEN;
const defaultSender = process.env.DEFAULT_SENDER_EMAIL;
const defaultMessageStream = process.env.DEFAULT_MESSAGE_STREAM;

// Validate configuration
if (!serverToken) {
  throw new Error('POSTMARK_SERVER_TOKEN is not set. Please configure it in your .env file.');
}
// Additional validation...

const client = new postmark.ServerClient(serverToken);

Creating the MCP Server

const server = new McpServer({
  name: "postmark-mcp",
  version: "1.0.0"
});

Registering the Email Tool

The most important part is defining our sendEmail tool with its parameters and functionality:

server.tool(
  "sendEmail",
  {
    to: z.string().email(),
    subject: z.string(),
    textBody: z.string(),
    from: z.string().optional(),
    messageStream: z.string().optional()
  },
  async ({ to, subject, textBody, from, messageStream }) => {
    console.error('Received request to send email to:', to);
    const result = await client.sendEmail({
      From: from || defaultSender,
      To: to,
      Subject: subject,
      TextBody: textBody,
      MessageStream: messageStream || defaultMessageStream
    });
    console.error('Email sent successfully:', result.MessageID);
    return {
      content: [
        { type: "text", text: `Email sent! MessageID: ${result.MessageID}` }
      ]
    };
  }
);

This defines:

The tool name (sendEmail)
Parameter schema (required: to, subject, textBody; optional: from, messageStream)
The implementation function that calls Postmark’s API

Starting the Server

const transport = new StdioServerTransport();
server.connect(transport)
  .then(() => {
    console.error('Server connected successfully and is now listening for MCP requests.');
  })
  .catch(error => {
    console.error('Error connecting server:', error);
    process.exit(1);
  });

The server uses stdio for transport, making it easy to pipe input/output with other applications.

Using the Postmark MCP Server

Once the server is running, AI assistants or other applications that support MCP can use it to send emails by calling the sendEmail tool with the required parameters:

// Example call format (from an MCP client)
{
  "to": "recipient@example.com",
  "subject": "Hello from MCP",
  "textBody": "This email was sent through the Postmark MCP server!"
}

Benefits and Use Cases

This approach offers several advantages:

Simplicity: AI systems don’t need to understand Postmark’s API specifics
Standardization: Follows the MCP specification for interoperability
Configuration: Default values make integration easier
Security: Credentials are stored server-side, not exposed to AI models

Some potential use cases:

Allow AI assistants to send notifications or reports via email
Enable scheduled email sending through AI-driven workflows
Implement email confirmation systems in AI applications
Build AI-managed email campaigns with content generation

Future Enhancements

There are several ways this project could be extended:

Add support for HTML email content
Implement template-based emails using Postmark templates
Add attachment support
Create a more sophisticated permission model
Add support for tracking email opens and clicks

Conclusion

The Postmark MCP Server demonstrates how we can bridge the gap between AI systems and practical services like email. By wrapping Postmark’s powerful email capabilities in an MCP-compatible interface, we’ve created a reusable component that makes it easier to incorporate email functionality into AI workflows.

The project is available on GitHub at github.com/jaballer/postmark-mcp-server – contributions welcome!

Have you built any interesting MCP servers or integrated email services with AI systems? I’d love to hear about your experiences in the comments below.