How to integrate Mailsoftly MCP with LangChain

This guide walks you through connecting Mailsoftly to LangChain using the Composio tool router. By the end, you'll have a working Mailsoftly agent that can verify if your mailsoftly api key is valid, check our company profile details on mailsoftly, confirm mailsoftly account credentials before sending campaign through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Mailsoftly account through Composio's Mailsoftly MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

Mailsoftly logoMailsoftly
Api Key

Mailsoftly is an intuitive email marketing platform for businesses to create, schedule, and manage campaigns. It makes audience communication easy with automation and user-friendly tools.

20 Tools

Introduction

This guide walks you through connecting Mailsoftly to LangChain using the Composio tool router. By the end, you'll have a working Mailsoftly agent that can verify if your mailsoftly api key is valid, check our company profile details on mailsoftly, confirm mailsoftly account credentials before sending campaign through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Mailsoftly account through Composio's Mailsoftly MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Mailsoftly with

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Mailsoftly project to Composio
  • Create a Tool Router MCP session for Mailsoftly
  • Initialize an MCP client and retrieve Mailsoftly tools
  • Build a LangChain agent that can interact with Mailsoftly
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

What is the Mailsoftly MCP server, and what's possible with it?

The Mailsoftly MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Mailsoftly account. It provides structured and secure access to your email marketing platform, so your agent can validate credentials, access firm information, and streamline campaign setup for you.

  • API key authentication validation: Have your agent confirm your API key is working and securely connected before taking further actions.
  • Retrieve firm details: Instantly pull details about your Mailsoftly organization to verify account information or tailor campaign strategies.
  • Credential troubleshooting automation: Let your agent proactively diagnose and report authentication issues, making onboarding and integration smoother.
  • Foundational checks for workflow automation: Use the agent to verify Mailsoftly connection status as the first step in building automated campaign flows.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step10 STEPS
1

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • @composio/langchain provides Composio integration for LangChain
  • @langchain/mcp-adapters enables MCP client connections
  • @langchain/core is the core agent framework
  • dotenv/config loads environment variables
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models
5

Import dependencies

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv/config import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Mailsoftly functionality through MCP
6

Initialize Composio client

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Mailsoftly tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding
7

Create a Tool Router session

const session = await composio.create(
    userId as string,
    {
        toolkits: ['mailsoftly']
    }
);

const url = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Mailsoftly tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
  • This approach allows the agent to dynamically load and use Mailsoftly tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "mailsoftly-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Mailsoftly MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Mailsoftly tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model
9

Set up interactive chat interface

let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Mailsoftly related question or task to the agent.\n");

const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: '
});

rl.prompt();

rl.on('line', async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
        console.log("\nGoodbye!");
        rl.close();
        process.exit(0);
    }

    if (!trimmedInput) {
        rl.prompt();
        return;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
What's happening:
  • We initialize an empty conversationHistory list to maintain context across interactions
  • A readline interface is used to continuously accept user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the invoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully
10

Run the application

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
What's happening:
  • We call the main() function to start the application

Complete Code

Here's the complete code to get you started with Mailsoftly and LangChain:

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['mailsoftly']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "mailsoftly-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Mailsoftly related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    rl.prompt();

    rl.on('line', async (userInput: string) => {
        const trimmedInput = userInput.trim();
        
        if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
            console.log("\nGoodbye!");
            rl.close();
            process.exit(0);
        }
        
        if (!trimmedInput) {
            rl.prompt();
            return;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});

Conclusion

You've successfully built a LangChain agent that can interact with Mailsoftly through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.
TOOLS

Supported Tools

Every Mailsoftly action and event your agent gets out of the box.

Add Contact to Contact List

Tool to add an existing contact to a specific contact list.

Add Custom Field to Contact

Tool to add a custom field value to an existing contact.

Assign Tags to Contact

Tool to assign multiple tags to an existing contact at once.

Authenticate Firm

Tool to validate the API key and retrieve firm details.

Create Contact

Tool to create a new contact for the authenticated firm with email, first name, and last name.

Create Contact List

Tool to create a new contact list for the authenticated firm.

Create Email Drafts

Tool to create one or more email drafts in Mailsoftly.

Create or Update Contact

Tool to create a new contact or update an existing contact in Mailsoftly.

Get Contact

Tool to retrieve a specific contact by ID from Mailsoftly.

Get Contact Fields

Tool to fetch all contact fields available for a firm, excluding hidden columns.

Get Contact List

Tool to retrieve details of a specific contact list by ID.

Get Contact List Contacts

Tool to retrieve all contacts within a specific contact list.

Get Contact Lists

Tool to retrieve all contact lists for the authenticated firm.

Get Contacts

Tool to retrieve all contacts associated with a firm.

Get Custom Fields

Tool to retrieve all custom fields defined for the authenticated firm.

Get Email Status

Tool to fetch the status of a specific email draft by its ID.

Get Tags

Tool to retrieve all tags associated with the authenticated firm.

Search Contacts

Tool to search for contacts matching specified criteria in Mailsoftly.

Send Email

Tool to send an existing email draft that is marked as ready.

Update Contact

Tool to update an existing contact's information in Mailsoftly.

FAQ

Frequently asked questions

With a standalone Mailsoftly MCP server, the agents and LLMs can only access a fixed set of Mailsoftly tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Mailsoftly and many other apps based on the task at hand, all through a single MCP endpoint.

Yes, you can. LangChain fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Mailsoftly tools.

Yes, absolutely. You can configure which Mailsoftly scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Mailsoftly data and credentials are handled as safely as possible.

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