How to integrate Bigmailer MCP with Vercel AI SDK v6

This guide walks you through connecting Bigmailer to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Bigmailer agent that can create a new welcome campaign for brand x, list all brands i manage in bigmailer, get your bigmailer account user details through natural language commands. This guide will help you understand how to give your Vercel AI SDK agent real control over a Bigmailer account through Composio's Bigmailer MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

Bigmailer logoBigmailer
Api Key

BigMailer is an email marketing platform for managing multiple brands with white-labeling and automation. It helps teams streamline campaigns and simplify integration with Amazon SES.

57 Tools

Introduction

This guide walks you through connecting Bigmailer to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Bigmailer agent that can create a new welcome campaign for brand x, list all brands i manage in bigmailer, get your bigmailer account user details through natural language commands.

This guide will help you understand how to give your Vercel AI SDK agent real control over a Bigmailer account through Composio's Bigmailer MCP server.

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

Also integrate Bigmailer with

TL;DR

Here's what you'll learn:
  • How to set up and configure a Vercel AI SDK agent with Bigmailer integration
  • Using Composio's Tool Router to dynamically load and access Bigmailer 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 Bigmailer MCP server, and what's possible with it?

The Bigmailer MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Bigmailer account. It provides structured and secure access to your email marketing platform, so your agent can perform actions like creating transactional campaigns, retrieving your brands, and managing user account details on your behalf.

  • Automated transactional campaign creation: Have your agent quickly set up new transactional email campaigns for any of your brands, with full control over content, sender details, and subject lines.
  • Brand management and discovery: Let your agent list and organize all brands associated with your Bigmailer account, providing a clear overview for multi-brand operations.
  • User account information retrieval: Easily check your authenticated user details to verify API connectivity and view essential account information in real time.
  • Multi-brand marketing workflow automation: Empower your agent to streamline campaign launches and brand management across multiple business entities from one place.

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 step09 STEPS
1

Prerequisites

Before you begin, make sure you have:
  • Node.js and npm installed
  • A Composio account with API key
  • An OpenAI API key
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 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
4

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
5

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
6

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: ["bigmailer"],
  });

  const mcpUrl = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Bigmailer 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 Bigmailer-related tools through the MCP protocol
7

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 Bigmailer tools that the agent can use
8

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 bigmailer, 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
9

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 Bigmailer 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 Bigmailer 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: ["bigmailer"],
  });

  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 bigmailer, 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 Bigmailer 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 Bigmailer action and event your agent gets out of the box.

Create Brand

Tool to create a new brand in BigMailer.

Create Brand Property

Tool to create a brand property in BigMailer.

Create Bulk Campaign

Tool to create a bulk email campaign in BigMailer.

Create Contact

Tool to create a new contact in BigMailer within a specified brand.

Create Contact Batch

Tool to create a batch of contacts in BigMailer for a specific brand.

Create Field

Tool to create a custom field in a BigMailer brand.

Create List

Creates a new contact list within a specified brand in BigMailer.

Create Segment

Tool to create a segment in BigMailer for a specific brand.

Create Suppression List

Tool to upload a suppression list for a brand in BigMailer.

Create Template

Tool to create a new email or page template in BigMailer.

Create Transactional Campaign

Creates a new transactional campaign within a specified brand in BigMailer.

Create User

Tool to create a new user in BigMailer.

Delete Brand Property

Tool to delete a brand property from a brand in BigMailer.

Delete Contact

Tool to delete a contact from a brand in BigMailer.

Delete Custom Field

Deletes a custom field from a specified brand in BigMailer.

Delete List

Tool to delete a list from BigMailer.

Delete Segment

Tool to delete a segment from a brand in BigMailer.

Delete Template

Tool to delete a template from BigMailer.

Delete User

Tool to delete a user from BigMailer.

Get Brand

Tool to retrieve detailed information about a specific brand by its ID.

Get Brand Property

Tool to retrieve a specific brand property by its ID for a given brand.

Get Bulk Campaign

Tool to retrieve detailed information about a specific bulk campaign in BigMailer.

Get Contact

Tool to retrieve detailed information about a specific contact from BigMailer.

Get Contact Batch Status

Tool to retrieve the status and results of a contact batch upload in BigMailer.

Get Custom Field

Tool to retrieve a custom field from a BigMailer brand.

Get List

Tool to retrieve details of a specific list within a brand.

Get Segment

Tool to retrieve a specific segment from BigMailer by brand ID and segment ID.

Get Suppression List

Tool to retrieve details of a specific suppression list for a brand in BigMailer.

Get Template

Tool to retrieve detailed information about a specific template by its ID.

Get Transactional Campaign

Tool to retrieve detailed information about a specific transactional campaign in BigMailer.

Get User

Tool to retrieve detailed information about a specific user by their ID.

Get User Information

This tool retrieves information about the authenticated user in BigMailer using the GET /me endpoint.

List All Brands

This tool retrieves a list of all brands associated with the authenticated BigMailer account.

List Brand Properties

Tool to retrieve a list of brand properties for a specific brand in BigMailer.

List Bulk Campaigns

Tool to list bulk campaigns for a specified brand in BigMailer.

List Connections

Tool to list all connections in your BigMailer account.

List Contacts

Tool to list contacts for a brand in BigMailer.

List Fields

Tool to list custom fields for a brand in BigMailer.

List Contact Lists

Tool to retrieve all contact lists for a specified brand in BigMailer.

List Message Types

Tool to list message types for a specific brand in BigMailer.

List Segments

Tool to list segments for a brand in BigMailer.

List Senders

Tool to list all senders configured for a specific brand in BigMailer.

List Suppression Lists

Tool to list suppression lists for a specific brand.

List Templates

Tool to list templates for a brand in BigMailer.

List Transactional Campaigns

Tool to list transactional campaigns for a specified brand in BigMailer.

List Users

Tool to list all users in your BigMailer account.

Update Brand

Tool to update a brand in BigMailer.

Update Brand Property

Tool to update a brand property in BigMailer.

Update Bulk Campaign

Tool to update an existing bulk campaign in BigMailer.

Update Contact

Tool to update an existing contact in BigMailer.

Update Field

Tool to update a custom field in BigMailer.

Update List

Tool to update a list in BigMailer.

Update Segment

Tool to update an existing segment in BigMailer.

Update Template

Tool to update an existing email or page template in BigMailer.

Update Transactional Campaign

Tool to update a transactional campaign in BigMailer.

Update User

Tool to update a user in BigMailer.

Upsert Contact

Tool to create or update a contact in a BigMailer brand.

FAQ

Frequently asked questions

With a standalone Bigmailer MCP server, the agents and LLMs can only access a fixed set of Bigmailer tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Bigmailer 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 Bigmailer tools.

Yes, absolutely. You can configure which Bigmailer 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 Bigmailer data and credentials are handled as safely as possible.

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