# How to integrate Emaillistverify MCP with Vercel AI SDK v6

```json
{
  "title": "How to integrate Emaillistverify MCP with Vercel AI SDK v6",
  "toolkit": "Emaillistverify",
  "toolkit_slug": "emaillistverify",
  "framework": "Vercel AI SDK",
  "framework_slug": "ai-sdk",
  "url": "https://composio.dev/toolkits/emaillistverify/framework/ai-sdk",
  "markdown_url": "https://composio.dev/toolkits/emaillistverify/framework/ai-sdk.md",
  "updated_at": "2026-05-12T10:10:14.160Z"
}
```

## Introduction

This guide walks you through connecting Emaillistverify to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Emaillistverify agent that can check if this email address is valid, get detailed deliverability info for an email, verify a user's email before signup through natural language commands.
This guide will help you understand how to give your Vercel AI SDK agent real control over a Emaillistverify account through Composio's Emaillistverify MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Emaillistverify with

- [OpenAI Agents SDK](https://composio.dev/toolkits/emaillistverify/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/emaillistverify/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/emaillistverify/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/emaillistverify/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/emaillistverify/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/emaillistverify/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/emaillistverify/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/emaillistverify/framework/cli)
- [Google ADK](https://composio.dev/toolkits/emaillistverify/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/emaillistverify/framework/langchain)
- [Mastra AI](https://composio.dev/toolkits/emaillistverify/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/emaillistverify/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/emaillistverify/framework/crew-ai)

## TL;DR

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

The Emaillistverify MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Emaillistverify account. It provides structured and secure access to your email verification tools, so your agent can validate addresses, check deliverability, and provide detailed insights into email list quality on your behalf.
- Real-time email verification: Instantly check if a single email address is valid and deliverable before adding it to your list or sending messages.
- Detailed email validation insights: Get in-depth reports on why an email address may be risky or undeliverable, including error types and validation reasons.
- Automated list hygiene: Quickly verify new signups or leads as they come in, helping you keep your email lists clean and up-to-date.
- Prevent bounced emails: Reduce hard bounces and protect your sender reputation by validating addresses before campaigns are sent.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `EMAILLISTVERIFY_CHECK_BLACKLISTS` | Check Blacklists | Tool to check an IP address (IPv4/IPv6) or domain against multiple DNS-based blacklists (DNSBLs) for spam or malicious activity. Use when you need to verify the reputation of an IP or domain. Rate limit: 10 requests/second. |
| `EMAILLISTVERIFY_CHECK_DISPOSABLE` | Check Disposable Domain | Tool to verify if an email domain is associated with temporary/disposable email addresses. Includes DNS record verification. Use when you need to validate if a domain is disposable before accepting email registrations. |
| `EMAILLISTVERIFY_DELETE_MAILLIST` | Delete Maillist | Tool to delete a finished email list. Use when you need to remove a completed verification list. Only lists that have completed verification can be deleted. |
| `EMAILLISTVERIFY_DOWNLOAD_MAILLIST` | Download Email List | Tool to download a finished email list with verification results. Supports customizable columns (firstName, lastName, gender, result, etc.) and file format (csv/xlsx). Rate limit: 5 requests/second. |
| `EMAILLISTVERIFY_FIND_CONTACT` | Find Contact Email | Tool to search for a contact's business email address by name and company domain. Returns possible emails with confidence levels (high/medium/low/unknown). Rate limit: 5 requests/second. Credits: 5 (with name) or 10 (domain only). |
| `EMAILLISTVERIFY_GET_API_FILE_INFO` | Get API File Info | Tool to retrieve progress of an uploaded email list verification. Returns status (errored/waiting/progress/finished) with download URLs when complete. |
| `EMAILLISTVERIFY_GET_CREDITS` | Get Credits | Tool to retrieve details about available on-demand and subscription credits. Use when you need to check credit balance before performing verifications. On-demand credits never expire, subscription credits are refreshed daily. Rate limit: 10 requests/second. |
| `EMAILLISTVERIFY_GET_EMAIL_JOB` | Get Email Job Status | Tool to get the status of an asynchronous email verification job. Use when you need to check if a verification job has completed and retrieve its results. Rate limit: 100 requests per second. |
| `EMAILLISTVERIFY_GET_MAILLIST_PROGRESS` | Get Maillist Progress | Tool to retrieve real-time progress updates for an uploaded email list verification. Shows status, completion percentage, and credit usage. Rate limit: 100 requests/second. |
| `EMAILLISTVERIFY_UPLOAD_EMAIL_LIST` | Upload Email List | Tool to upload an email list file for bulk verification. Accepts .csv, .txt, or .xlsx files (max 100MB, 1M rows). Returns an ID to query verification progress. Rate limit: 5 requests/second. |
| `EMAILLISTVERIFY_VERIFY_SINGLE_EMAIL` | Verify Single Email | Tool to verify email deliverability status of a single email address. Returns a plain text status representing deliverability. Rate limit: 10 requests/second. Credits required: 1. |
| `EMAILLISTVERIFY_VERIFY_SINGLE_EMAIL_DETAILED` | Verify Single Email Detailed | Tool to verify email deliverability with detailed metadata including MX server info, ESP, first/last name estimation, gender, and role detection. Use when you need comprehensive email validation beyond basic deliverability. Rate limit: 10 requests/second, requires 1 credit per verification. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Emaillistverify MCP server is an implementation of the Model Context Protocol that connects your AI agent to Emaillistverify. It provides structured and secure access so your agent can perform Emaillistverify operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

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

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install required dependencies

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

### 3. Set up environment variables

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
```bash
OPENAI_API_KEY=your_openai_api_key_here
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here
```

### 4. Import required modules and validate environment

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
```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,
});
```

### 5. Create Tool Router session and initialize MCP client

What's happening:
- We're creating a Tool Router session that gives your agent access to Emaillistverify 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 Emaillistverify-related tools through the MCP protocol
```typescript
async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["emaillistverify"],
  });

  const mcpUrl = session.mcp.url;
```

### 6. Connect to MCP server and retrieve 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 Emaillistverify tools that the agent can use
```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();
```

### 7. Initialize conversation and CLI interface

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
```typescript
let messages: ModelMessage[] = [];

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

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

rl.prompt();
```

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

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 Emaillistverify 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
```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);
});
```

## Complete Code

```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: ["emaillistverify"],
  });

  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 emaillistverify, 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 Emaillistverify 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.

## How to build Emaillistverify MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/emaillistverify/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/emaillistverify/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/emaillistverify/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/emaillistverify/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/emaillistverify/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/emaillistverify/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/emaillistverify/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/emaillistverify/framework/cli)
- [Google ADK](https://composio.dev/toolkits/emaillistverify/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/emaillistverify/framework/langchain)
- [Mastra AI](https://composio.dev/toolkits/emaillistverify/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/emaillistverify/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/emaillistverify/framework/crew-ai)

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## Frequently Asked Questions

### What are the differences in Tool Router MCP and Emaillistverify MCP?

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

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

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 Emaillistverify tools.

### Can I manage the permissions and scopes for Emaillistverify while using Tool Router?

Yes, absolutely. You can configure which Emaillistverify 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.

### How safe is my data with Composio Tool Router?

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 Emaillistverify data and credentials are handled as safely as possible.

---
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