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How to integrate Mailsoftly MCP with Vercel AI SDK v6

Connect Vercel AI SDK v6 to Mailsoftly MCP. Verify if your mailsoftly api key is valid, check our company profile details on mailsoftly, and more using natural language, with authentication handled for you.

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Mailsoftly

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

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Introduction

This guide walks you through connecting Mailsoftly to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Mailsoftly agent that can manage contacts and lists, tag contacts, create email drafts, send emails, and check delivery status through natural language commands.

This guide will help you understand how to give your Vercel AI SDK 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

Claude Code Claude Cowork OpenAI Agents SDK Claude Agent SDK Codex OpenClaw Hermes Atomic Agent Google ADK LangChain Mastra AI LlamaIndex CrewAI Pydantic AI AutoGen

TL;DR

Here's what you'll learn:

How to set up and configure a Vercel AI SDK agent with Mailsoftly integration
Using Composio's Tool Router to dynamically load and access Mailsoftly tools
Creating an MCP client connection using HTTP transport
Building an interactive CLI chat interface with conversation history management
Handling tool calls and results within the Vercel AI SDK framework

What is Vercel AI SDK?

The Vercel AI SDK is a TypeScript library for building AI-powered applications. It provides tools for creating agents that can use external services and maintain conversation state.

Key features include:

streamText: Core function for streaming responses with real-time tool support
MCP Client: Built-in support for Model Context Protocol via @ai-sdk/mcp
Step Counting: Control multi-step tool execution with stopWhen: stepCountIs()
OpenAI Provider: Native integration with OpenAI models

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:

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

Step-by-step Guide

Step by step09 STEPS

Prerequisites

Before you begin, make sure you have:

Node.js and npm installed
A Composio account with API key
An OpenAI API key

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.

Install required dependencies

bash

npm install @ai-sdk/openai @ai-sdk/mcp @composio/core ai dotenv

First, install the necessary packages for your project.

What you're installing:

@ai-sdk/openai: Vercel AI SDK's OpenAI provider
@ai-sdk/mcp: MCP client for Vercel AI SDK
@composio/core: Composio SDK for tool integration
ai: Core Vercel AI SDK
dotenv: Environment variable management

Set up environment variables

bash

OPENAI_API_KEY=your_openai_api_key_here
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here

Create a .env file in your project root.

What's needed:

OPENAI_API_KEY: Your OpenAI API key for GPT model access
COMPOSIO_API_KEY: Your Composio API key for tool access
COMPOSIO_USER_ID: A unique identifier for the user session

Import required modules and validate environment

typescript

import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Composio } from "@composio/core";
import * as readline from "readline";
import { streamText, type ModelMessage, stepCountIs } from "ai";
import { createMCPClient } from "@ai-sdk/mcp";

const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey,
});

What's happening:

We're importing all necessary libraries including Vercel AI SDK's OpenAI provider and Composio
The dotenv/config import automatically loads environment variables
The MCP client import enables connection to Composio's tool server

Create Tool Router session and initialize MCP client

typescript

async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["mailsoftly"],
  });

  const mcpUrl = 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 mcp object contains the URL and authentication headers needed to connect to the MCP server
This session provides access to all Mailsoftly-related tools through the MCP protocol

Connect to MCP server and retrieve tools

typescript

const mcpClient = await createMCPClient({
  transport: {
    type: "http",
    url: mcpUrl,
    headers: session.mcp.headers, // Authentication headers for the Composio MCP server
  },
});

const tools = await mcpClient.tools();

What's happening:

We're creating an MCP client that connects to our Composio Tool Router session via HTTP
The mcp.url provides the endpoint, and mcp.headers contains authentication credentials
The type: "http" is important - Composio requires HTTP transport
tools() retrieves all available Mailsoftly tools that the agent can use

Initialize conversation and CLI interface

typescript

let messages: ModelMessage[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log(
  "Ask any questions related to mailsoftly, like summarize my last 5 emails, send an email, etc... :)))\n",
);

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

rl.prompt();

What's happening:

We initialize an empty messages array to maintain conversation history
A readline interface is created to accept user input from the command line
Instructions are displayed to guide the user on how to interact with the agent

Handle user input and stream responses with real-time tool feedback

typescript

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;
  }

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

  try {
    const stream = streamText({
      model: openai("gpt-5"),
      messages,
      tools,
      toolChoice: "auto",
      stopWhen: stepCountIs(10),
      onStepFinish: (step) => {
        for (const toolCall of step.toolCalls) {
          console.log(`[Using tool: ${toolCall.toolName}]`);
          }
          if (step.toolCalls.length > 0) {
            console.log(""); // Add space after tool calls
          }
        },
      });

      for await (const chunk of stream.textStream) {
        process.stdout.write(chunk);
      }

      console.log("\n\n---\n");

      // Get final result for message history
      const response = await stream.response;
      if (response?.messages?.length) {
        messages.push(...response.messages);
      }
    } catch (error) {
      console.error("\nAn error occurred while talking to the agent:");
      console.error(error);
      console.log(
        "\nYou can try again or restart the app if it keeps happening.\n",
      );
    } finally {
      rl.prompt();
    }
  });

  rl.on("close", async () => {
    await mcpClient.close();
    console.log("\n👋 Session ended.");
    process.exit(0);
  });
}

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

What's happening:

We use streamText instead of generateText to stream responses in real-time
toolChoice: "auto" allows the model to decide when to use Mailsoftly tools
stopWhen: stepCountIs(10) allows up to 10 steps for complex multi-tool operations
onStepFinish callback displays which tools are being used in real-time
We iterate through the text stream to create a typewriter effect as the agent responds
The complete response is added to conversation history to maintain context
Errors are caught and displayed with helpful retry suggestions

Complete Code

Here's the complete code to get you started with Mailsoftly and Vercel AI SDK:

typescript

import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Composio } from "@composio/core";
import * as readline from "readline";
import { streamText, type ModelMessage, stepCountIs } from "ai";
import { createMCPClient } from "@ai-sdk/mcp";

const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey,
});

async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["mailsoftly"],
  });

  const mcpUrl = session.mcp.url;

  const mcpClient = await createMCPClient({
    transport: {
      type: "http",
      url: mcpUrl,
      headers: session.mcp.headers, // Authentication headers for the Composio MCP server
    },
  });

  const tools = await mcpClient.tools();

  let messages: ModelMessage[] = [];

  console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
  console.log(
    "Ask any questions related to mailsoftly, like summarize my last 5 emails, send an email, etc... :)))\n",
  );

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

  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;
    }

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

    try {
      const stream = streamText({
        model: openai("gpt-5"),
        messages,
        tools,
        toolChoice: "auto",
        stopWhen: stepCountIs(10),
        onStepFinish: (step) => {
          for (const toolCall of step.toolCalls) {
            console.log(`[Using tool: ${toolCall.toolName}]`);
          }
          if (step.toolCalls.length > 0) {
            console.log(""); // Add space after tool calls
          }
        },
      });

      for await (const chunk of stream.textStream) {
        process.stdout.write(chunk);
      }

      console.log("\n\n---\n");

      // Get final result for message history
      const response = await stream.response;
      if (response?.messages?.length) {
        messages.push(...response.messages);
      }
    } catch (error) {
      console.error("\nAn error occurred while talking to the agent:");
      console.error(error);
      console.log(
        "\nYou can try again or restart the app if it keeps happening.\n",
      );
    } finally {
      rl.prompt();
    }
  });

  rl.on("close", async () => {
    await mcpClient.close();
    console.log("\n👋 Session ended.");
    process.exit(0);
  });
}

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

Conclusion

You've successfully built a Mailsoftly agent using the Vercel AI SDK with streaming capabilities! This implementation provides a powerful foundation for building AI applications with natural language interfaces and real-time feedback.

Key features of this implementation:

Real-time streaming responses for a better user experience with typewriter effect
Live tool execution feedback showing which tools are being used as the agent works
Dynamic tool loading through Composio's Tool Router with secure authentication
Multi-step tool execution with configurable step limits (up to 10 steps)
Comprehensive error handling for robust agent execution
Conversation history maintenance for context-aware responses

You can extend this further by adding custom 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

Retrieve a Mailsoftly contact by ID, returning its ID, email, first name and last name.

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.

FRAMEWORKS

How to build Mailsoftly MCP Agent with another framework

Claude Code

Use Mailsoftly MCP with Claude Code

Claude Cowork

Use Mailsoftly MCP with Claude Cowork

OpenAI Agents SDK

Use Mailsoftly MCP with OpenAI Agents SDK

Claude Agent SDK

Use Mailsoftly MCP with Claude Agent SDK

Codex

Use Mailsoftly MCP with Codex

OpenClaw

Use Mailsoftly MCP with OpenClaw

Hermes

Use Mailsoftly MCP with Hermes

Atomic Agent

Use Mailsoftly MCP with Atomic Agent

Google ADK

Use Mailsoftly MCP with Google ADK

LangChain

Use Mailsoftly MCP with LangChain

Mastra AI

Use Mailsoftly MCP with Mastra AI

LlamaIndex

Use Mailsoftly MCP with LlamaIndex

CrewAI

Use Mailsoftly MCP with CrewAI

Pydantic AI

Use Mailsoftly MCP with Pydantic AI

AutoGen

Use Mailsoftly MCP with AutoGen

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Active campaign

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ActiveCampaign is a marketing automation and CRM platform for managing email campaigns, sales pipelines, and customer segmentation. It helps businesses engage customers and drive growth through smart automation and targeted outreach.

ActiveTrail

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ActiveTrail is a user-friendly email marketing and automation platform. It helps you reach subscribers and automate campaigns with ease.

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. Vercel AI SDK v6 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.

Start with Mailsoftly.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Mailsoftly tool your agent needs.Free to start.

Start building

How to integrate Mailsoftly MCP with Vercel AI SDK v6

Connect Mailsoftly without auth hassles

Introduction

Also integrate Mailsoftly with

TL;DR

What is Vercel AI SDK?

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

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

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install required dependencies

Set up environment variables

Import required modules and validate environment

Create Tool Router session and initialize MCP client

Connect to MCP server and retrieve tools

Initialize conversation and CLI interface

Handle user input and stream responses with real-time tool feedback

Complete Code

Conclusion

Supported Tools

How to build Mailsoftly MCP Agent with another framework

Claude Code

Claude Cowork

OpenAI Agents SDK

Claude Agent SDK

Codex

OpenClaw

Hermes

Atomic Agent

Google ADK

LangChain

Mastra AI

LlamaIndex

CrewAI

Pydantic AI

AutoGen

Explore Other Toolkits

Reddit

Linkedin

Active campaign

ActiveTrail

Frequently asked questions

What are the differences in Tool Router MCP and Mailsoftly MCP?+

Can I use Tool Router MCP with Vercel AI SDK v6?+

Can I manage the permissions and scopes for Mailsoftly while using Tool Router?+

How safe is my data with Composio Tool Router?+

Start with Mailsoftly.It takes 30 seconds.