# How to integrate Clearout MCP with LangChain

```json
{
  "title": "How to integrate Clearout MCP with LangChain",
  "toolkit": "Clearout",
  "toolkit_slug": "clearout",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/clearout/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/clearout/framework/langchain.md",
  "updated_at": "2026-05-12T10:06:22.092Z"
}
```

## Introduction

This guide walks you through connecting Clearout to LangChain using the Composio tool router. By the end, you'll have a working Clearout agent that can validate a list of emails for deliverability, check if this email is a business account, find the most likely domain for acme corp through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Clearout account through Composio's Clearout MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Clearout with

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

## TL;DR

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

The Clearout MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Clearout account. It provides structured and secure access to email validation, prospecting, and enrichment tools, so your agent can perform actions like verifying email addresses, finding business contacts, checking domain details, and bulk-processing lists on your behalf.
- AI-powered email verification: Instantly validate single or bulk email addresses to ensure deliverability and reduce bounce rates.
- Email prospecting and enrichment: Find emails for people or companies, complete missing contact data, and verify if accounts are business or personal.
- Domain and company intelligence: Retrieve company domains from names, fetch MX records, or pull WHOIS information to understand your leads and their infrastructure.
- Disposable and catch-all detection: Check if an email is temporary or if a domain accepts all mail, helping you maintain list quality and avoid spam traps.
- Bulk job automation: Upload, process, monitor, cancel, and download results for large-scale email finding and verification tasks, all through your agent.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `CLEAROUT_AUTOCOMPLETE_COMPANY_TO_DOMAIN` | Autocomplete Company to Domain | Tool to autocomplete company names to probable domains with confidence scores. Use after obtaining a company name or URL to get suggestions. |
| `CLEAROUT_BUSINESS_ACCOUNT_VERIFY` | Business Account Verify | Tool to check if an email belongs to a business/work account. Use when validating corporate emails before onboarding. |
| `CLEAROUT_CATCH_ALL_VERIFY` | Catch-All Verify | Tool to check if an email domain is catch-all. Use after confirming email validity. |
| `CLEAROUT_DISPOSABLE_VERIFY` | Verify Disposable Email | Tool to check if an email is from a disposable provider. Use after acquiring an email address. |
| `CLEAROUT_DOMAIN_FIND_MX` | Find Domain MX Records | Tool to retrieve MX records for a domain in priority order. Use when you need to find a domain's mail servers (e.g., for email routing setup). |
| `CLEAROUT_DOMAIN_FIND_WHOIS` | Fetch Domain WHOIS Information | Tool to fetch WHOIS record for a domain. Use when you need WHOIS data for a domain. |
| `CLEAROUT_EMAIL_FINDER_BULK` | Bulk Email Finder | Tool to upload a CSV or XLSX contacts file for bulk email finding. Use when you need to find emails for a list of contacts in bulk. |
| `CLEAROUT_EMAIL_FINDER_BULK_CANCEL` | Cancel Bulk Email Finder Job | Tool to cancel a running bulk email finder job. Use when you need to stop an in-progress list scan before completion. |
| `CLEAROUT_EMAIL_FINDER_BULK_RESULT_DOWNLOAD` | Bulk Email Finder Result Download | Tool to generate a bulk email finder result download URL. Use after confirming bulk job completion to retrieve the result file link. |
| `CLEAROUT_EMAIL_VERIFY_BULK` | Bulk Email Verify | Tool to upload a CSV or XLSX file for bulk email verification. Use when you have a list of emails to verify in bulk. |
| `CLEAROUT_EMAIL_VERIFY_BULK_CANCEL` | Cancel Bulk Email Verification Job | Cancel an in-progress bulk email verification job. Use this to stop a running verification before it completes, saving credits for unprocessed emails. The job must be in a cancellable state (not already completed or cancelled). Returns error code 1029 if list doesn't exist, or error code 1116 if the list is not in a cancellable stage. |
| `CLEAROUT_EMAIL_VERIFY_BULK_PROGRESS_STATUS` | Bulk Email Verify Progress Status | Tool to retrieve progress for a bulk email verification job. Use after initiating a bulk verification to poll its state and percent complete. |
| `CLEAROUT_EMAIL_VERIFY_BULK_RESULT_DOWNLOAD` | Bulk Email Verify Result Download | Tool to obtain a temporary URL for bulk email verification results. Use after completing a bulk verification job to download the results file. |
| `CLEAROUT_EMAIL_VERIFY_GET_CREDITS` | Email Verify Get Credits | Tool to fetch available email verification credits. Use when checking remaining credits before performing email verifications. |
| `CLEAROUT_EMAIL_VERIFY_INSTANT` | Instant Email Verifier | Tool to instantly verify a single email address. Use when you need real-time validation before processing an email. |
| `CLEAROUT_FREE_ACCOUNT_VERIFY` | Verify Free Email Account | Tool to detect if an email is from a free email service provider. Use after confirming the email format. |
| `CLEAROUT_GIBBERISH_ACCOUNT_VERIFY` | Verify Gibberish Email | Tool to verify if an email address is gibberish. Use when filtering out nonsensical or invalid-looking emails. |
| `CLEAROUT_REVERSE_LOOKUP_FIND_COMPANY_VIA_DOMAIN` | Reverse Lookup Company by Domain | Find company information (name, logo, LinkedIn URL, address) by looking up its domain name. Returns company profile data if found, or an error with code 5025 if no profile exists for the domain. |
| `CLEAROUT_REVERSE_LOOKUP_FIND_PERSON_VIA_EMAIL` | Reverse Lookup Person by Email | Tool to retrieve a person’s profile from an email address. Use when you want to enrich a valid email with associated person details. |
| `CLEAROUT_REVERSE_LOOKUP_FIND_PERSON_VIA_LINKED_IN` | Find Person via LinkedIn URL | Tool to discover person information via a LinkedIn profile URL. Use when you need to retrieve person’s profile details from a LinkedIn URL. |
| `CLEAROUT_ROLE_ACCOUNT_VERIFY` | Role Account Verifier | Tool to determine if an email is a role-based account. Use when identifying group mailboxes (e.g., support@) before sending targeted communications. |

## Supported Triggers

None listed.

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

The Clearout MCP server is an implementation of the Model Context Protocol that connects your AI agent to Clearout. It provides structured and secure access so your agent can perform Clearout 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 Clearout 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 Clearout 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 Clearout tools as needed
```python
# Create Tool Router session for Clearout
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['clearout']
)

url = session.mcp.url
```

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

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

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

No description provided.
```python
client = MultiServerMCPClient({
    "clearout-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({
    "clearout-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 Clearout 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 Clearout 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=['clearout']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "clearout-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 Clearout 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: ['clearout']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "clearout-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 Clearout 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 Clearout 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 Clearout MCP Agent with another framework

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

## Related Toolkits

- [Aeroleads](https://composio.dev/toolkits/aeroleads) - Aeroleads is a B2B lead generation platform for finding business emails and phone numbers. Grow your sales pipeline faster with powerful prospecting tools.
- [Autobound](https://composio.dev/toolkits/autobound) - Autobound is an AI-powered sales engagement platform that crafts hyper-personalized outreach and insights. It helps sales teams boost response rates and close more deals through tailored content and recommendations.
- [Better proposals](https://composio.dev/toolkits/better_proposals) - Better Proposals is a web-based tool for crafting and sending professional proposals. It helps teams impress clients and close deals faster with slick, easy-to-use templates.
- [Bidsketch](https://composio.dev/toolkits/bidsketch) - Bidsketch is a proposal software that helps businesses create professional proposals quickly and efficiently. It streamlines the proposal process, saving time while boosting client win rates.
- [Bolna](https://composio.dev/toolkits/bolna) - Bolna is an AI platform for building conversational voice agents. It helps businesses automate support and streamline interactions through natural, voice-powered conversations.
- [Botsonic](https://composio.dev/toolkits/botsonic) - Botsonic is a no-code AI chatbot builder for easily creating and deploying chatbots to your website. It empowers businesses to offer conversational experiences without writing code.
- [Botstar](https://composio.dev/toolkits/botstar) - BotStar is a comprehensive chatbot platform for designing, developing, and training chatbots visually on Messenger and websites. It helps businesses automate conversations and customer interactions without coding.
- [Callerapi](https://composio.dev/toolkits/callerapi) - CallerAPI is a white-label caller identification platform for branded caller ID and fraud prevention. It helps businesses boost customer trust while stopping spam, fraud, and robocalls.
- [Callingly](https://composio.dev/toolkits/callingly) - Callingly is a lead response management platform that automates immediate call and text follow-ups with new leads. It helps sales teams boost response speed and close more deals by connecting seamlessly with CRMs and lead sources.
- [Callpage](https://composio.dev/toolkits/callpage) - Callpage is a lead capture platform that lets businesses instantly connect with website visitors via callback. It boosts lead generation and increases your sales conversion rates.
- [Clientary](https://composio.dev/toolkits/clientary) - Clientary is a platform for managing clients, invoices, projects, proposals, and more. It streamlines client work and saves you serious admin time.
- [Convolo ai](https://composio.dev/toolkits/convolo_ai) - Convolo ai is an AI-powered communications platform for sales teams. It accelerates lead response and improves conversion rates by automating calls and integrating workflows.
- [Delighted](https://composio.dev/toolkits/delighted) - Delighted is a customer feedback platform based on the Net Promoter System®. It helps you quickly gather, track, and act on customer sentiment.
- [Docsbot ai](https://composio.dev/toolkits/docsbot_ai) - Docsbot ai is a platform that lets you build custom AI chatbots trained on your documentation. It automates customer support and content generation, saving time and improving response quality.
- [Emelia](https://composio.dev/toolkits/emelia) - Emelia is an all-in-one B2B prospecting platform for cold-email, LinkedIn outreach, and prospect research. It streamlines outbound campaigns so you can find, engage, and warm up leads faster.
- [Findymail](https://composio.dev/toolkits/findymail) - Findymail is a B2B data provider offering verified email and phone contacts for sales prospecting. Enhance outreach with automated exports, email verification, and CRM enrichment.
- [Freshdesk](https://composio.dev/toolkits/freshdesk) - Freshdesk is customer support software with ticketing and automation tools. It helps teams streamline helpdesk operations for faster, better customer support.
- [Fullenrich](https://composio.dev/toolkits/fullenrich) - FullEnrich is a B2B contact enrichment platform that aggregates emails and phone numbers from 15+ data vendors. Instantly find and verify lead contact data to boost your outreach.
- [Gatherup](https://composio.dev/toolkits/gatherup) - GatherUp is a customer feedback and online review management platform. It helps businesses boost their reputation by streamlining how they collect and manage customer feedback.
- [Getprospect](https://composio.dev/toolkits/getprospect) - Getprospect is a business email discovery tool with LinkedIn integration. Use it to quickly find and verify professional email addresses.

## Frequently Asked Questions

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

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

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

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

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