# How to integrate Listclean MCP with LangChain

```json
{
  "title": "How to integrate Listclean MCP with LangChain",
  "toolkit": "Listclean",
  "toolkit_slug": "listclean",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/listclean/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/listclean/framework/langchain.md",
  "updated_at": "2026-05-12T10:17:46.266Z"
}
```

## Introduction

This guide walks you through connecting Listclean to LangChain using the Composio tool router. By the end, you'll have a working Listclean agent that can verify if this email address is valid, check how many email credits i have left, create a csv for bulk email verification through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Listclean account through Composio's Listclean MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Listclean with

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

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Connect your Listclean project to Composio
- Create a Tool Router MCP session for Listclean
- Initialize an MCP client and retrieve Listclean tools
- Build a LangChain agent that can interact with Listclean
- 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 Listclean MCP server, and what's possible with it?

The Listclean MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Listclean account. It provides structured and secure access to your email verification tools, so your agent can perform actions like verifying emails, managing bulk verification lists, checking credits, and retrieving account info on your behalf.
- Single email verification: Instantly check if an email address is valid, deliverable, or disposable—perfect for real-time signups or contact forms.
- Bulk email list processing: Have your agent generate and upload CSV files of email addresses for large-scale verification and list cleaning.
- Account credit monitoring: Easily monitor your remaining verification credits so your automations never hit a surprise limit.
- Profile data retrieval: Let your agent fetch and review your Listclean account profile details for audits or reporting purposes.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `LISTCLEAN_CREATE_BULK_VERIFICATION_LIST` | Create Bulk Verification List | Tool to create a CSV file with provided email addresses for bulk verification. Use when you need to generate a file for bulk upload through LISTCLEAN_UPLOAD_LIST. |
| `LISTCLEAN_DELETE_LIST` | Delete List | Tool to permanently delete a single list from the account. Use when you need to remove a list that is no longer needed. |
| `LISTCLEAN_DOWNLOAD_CSV` | Download List Results as CSV | Tool to download list results as a CSV file. Use when you need to retrieve verification results filtered by email type (clean, dirty, or unknown). |
| `LISTCLEAN_DOWNLOAD_JSON` | Download List Results as JSON | Tool to download list results as JSON. Downloads emails filtered by type (clean, dirty, or unknown) in JSON format. Use after list verification is complete to retrieve filtered results. |
| `LISTCLEAN_GET_ACCOUNT_PROFILE` | Get Account Profile | Tool to retrieve the authenticated account's profile. Use after obtaining a valid auth token to fetch user account data. |
| `LISTCLEAN_GET_CREDITS` | Get Remaining Credits | Tool to retrieve remaining verification credits. Use when you need to check your available account credits before performing more email verifications. |
| `LISTCLEAN_GET_LIST` | Get List Information | Tool to retrieve detailed information for a specific list. Returns analytics with clean/dirty breakdown, cost details, and processing status. |
| `LISTCLEAN_GET_UPLOAD_STATUS` | Get Upload Status | Tool to retrieve the status of a specific upload. Returns upload status (inprocess, success, error), progress percentage, and chunk details. Use after initiating an upload to track its completion. |
| `LISTCLEAN_GET_VERIFICATION_LOGS` | Get Verification Logs | Tool to retrieve logs for all single email verifications. Returns history of email verifications with status, remarks, credits deducted, and timestamps. |
| `LISTCLEAN_LIST_ALL` | List All Verification Lists | Tool to retrieve all email verification lists. Returns all processed lists with complete analytics including clean/dirty counts, summary statistics, and cost information. |
| `LISTCLEAN_LIST_UPLOADS` | List CSV Uploads | Tool to retrieve the list of CSV uploads. Use when you need to check upload IDs, status, and progress for all CSV file uploads in your account. |
| `LISTCLEAN_START_UPLOAD` | Start Upload | Tool to start a CSV upload process for bulk email verification. Initializes chunked file upload and returns an upload_id for subsequent chunk uploads. Use when you need to upload large CSV files containing email addresses. |
| `LISTCLEAN_UPDATE_PROFILE` | Update Account Profile | Tool to update account profile details. Use when you need to modify user profile information such as name, address, contact details, company information, or billing details. |
| `LISTCLEAN_UPLOAD_CHUNK` | Upload Chunk | Tool to upload a chunk of a CSV file. Use when sending base64-encoded content with sequence number as part of chunked upload process, optionally with MD5 checksum for integrity verification. |
| `LISTCLEAN_VERIFY_BATCH` | Verify Batch of Emails | Tool to verify a batch of email addresses (max 3000 emails). Use when you need to verify multiple emails at once and get a list_id for tracking the batch verification request. |
| `LISTCLEAN_VERIFY_EMAIL` | Verify Email Address | Tool to verify an email's validity. Use when you need to ensure an address is deliverable and non-disposable, after collecting a user's email. |

## Supported Triggers

None listed.

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

The Listclean MCP server is an implementation of the Model Context Protocol that connects your AI agent to Listclean. It provides structured and secure access so your agent can perform Listclean 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

No description provided.

### 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 dependencies

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

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

### 3. Set up environment variables

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
```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
```

### 4. Import dependencies

No description provided.
```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
```

```typescript
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();
```

### 5. Initialize Composio client

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 Listclean tools
- Validating that COMPOSIO_USER_ID is also set before proceeding
```python
async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
```

```typescript
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()
    });
```

### 6. Create a Tool Router session

What's happening:
- We're creating a Tool Router session that gives your agent access to Listclean 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 Listclean tools as needed
```python
# Create Tool Router session for Listclean
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['listclean']
)

url = session.mcp.url
```

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

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

### 7. Configure the agent with the MCP URL

No description provided.
```python
client = MultiServerMCPClient({
    "listclean-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
```

```typescript
const client = new MultiServerMCPClient({
    "listclean-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 });
```

### 8. Set up interactive chat interface

No description provided.
```python
conversation_history = []

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

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
```

```typescript
let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Listclean 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);
    });
```

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

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

## Complete Code

```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['listclean']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "listclean-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Listclean related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
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: ['listclean']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "listclean-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 Listclean 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 Listclean 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.

## How to build Listclean MCP Agent with another framework

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

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- [ActiveTrail](https://composio.dev/toolkits/active_trail) - ActiveTrail is a user-friendly email marketing and automation platform. It helps you reach subscribers and automate campaigns with ease.
- [Ahrefs](https://composio.dev/toolkits/ahrefs) - Ahrefs is an SEO and marketing platform for site audits, keyword research, and competitor insights. It helps you improve search rankings and drive organic traffic.
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- [Benchmark email](https://composio.dev/toolkits/benchmark_email) - Benchmark Email is a platform for creating, sending, and tracking email campaigns. It's built to help you engage audiences and analyze results—all in one place.
- [Bigmailer](https://composio.dev/toolkits/bigmailer) - 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.
- [Brandfetch](https://composio.dev/toolkits/brandfetch) - Brandfetch is an API that delivers company logos, colors, and visual branding assets. It helps marketers and developers keep brand visuals consistent everywhere.
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- [Campayn](https://composio.dev/toolkits/campayn) - Campayn is an email marketing platform for creating, sending, and managing campaigns. It helps businesses engage contacts and grow audiences with easy-to-use tools.
- [Cardly](https://composio.dev/toolkits/cardly) - Cardly is a platform for creating and sending personalized direct mail to customers. It helps businesses break through the digital clutter by getting real engagement via physical mailboxes.
- [ClickSend](https://composio.dev/toolkits/clicksend) - ClickSend is a cloud-based SMS and email marketing platform for businesses. It streamlines communication by enabling quick message delivery and contact management.
- [Crustdata](https://composio.dev/toolkits/crustdata) - CrustData is an AI-powered data intelligence platform for real-time company and people data. It helps B2B sales teams, AI SDRs, and investors react to live business signals.
- [Curated](https://composio.dev/toolkits/curated) - Curated is a platform for collecting, curating, and publishing newsletters. It streamlines content aggregation and distribution for creators and teams.
- [Customerio](https://composio.dev/toolkits/customerio) - Customer.io is a customer engagement platform for targeted messaging across email, SMS, and push. Easily automate, segment, and track communications with your audience.
- [Cutt ly](https://composio.dev/toolkits/cutt_ly) - Cutt.ly is a URL shortening service for managing and analyzing links. Streamline your workflows with quick, trackable, and branded short URLs.
- [Demio](https://composio.dev/toolkits/demio) - Demio is webinar software built for marketers, offering both live and automated sessions with interactive features. It helps teams engage audiences and optimize lead generation through detailed analytics.

## Frequently Asked Questions

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

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

### Can I use Tool Router MCP with LangChain?

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 Listclean tools.

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

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

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)