# How to integrate Bouncer MCP with LangChain

```json
{
  "title": "How to integrate Bouncer MCP with LangChain",
  "toolkit": "Bouncer",
  "toolkit_slug": "bouncer",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/bouncer/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/bouncer/framework/langchain.md",
  "updated_at": "2026-05-06T08:03:42.497Z"
}
```

## Introduction

This guide walks you through connecting Bouncer to LangChain using the Composio tool router. By the end, you'll have a working Bouncer agent that can verify this email address instantly, check domain validity for new signups, batch-verify a list of customer emails through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Bouncer account through Composio's Bouncer MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Bouncer with

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

## TL;DR

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

The Bouncer MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Bouncer account. It provides structured and secure access to your email verification and validation tools, so your agent can perform actions like real-time email validation, bulk verification, domain checks, and toxicity analysis for improved email deliverability.
- Real-time email verification: Instantly validate single email addresses to check deliverability and reduce bounce rates before sending messages.
- Bulk batch verification: Initiate, manage, and retrieve results from batch email verification jobs to clean and maintain large email lists efficiently.
- Toxicity analysis of email lists: Start and monitor toxicity analysis jobs to identify potentially harmful or problematic email addresses in your database.
- Domain verification: Check the validity and configuration of email domains, including MX records and catch-all status, to ensure emails reach their intended targets.
- Automated batch management: Finish, delete, or update batch verification and toxicity jobs to keep your verification workflows tidy and up-to-date.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `BOUNCER_CHECK_TOXICITY_LIST_JOB_STATUS` | Check Toxicity List Job Status | Tool to check the status of a specific toxicity list job. use after creating a toxicity list job to poll its status until completion. |
| `BOUNCER_CREATE_BATCH_REQUEST` | Create Batch Request | Tool to initiate a batch email verification request. use when you have multiple emails to verify in one api call. returns a batch id and initial status. |
| `BOUNCER_CREATE_TOXICITY_LIST_JOB` | Create Toxicity List Job | Tool to create a toxicity analysis job for a list of email addresses. use when you need to batch-process toxicity checks for multiple emails at once. |
| `BOUNCER_DELETE_BATCH_REQUEST` | Delete batch request | Tool to delete a batch verification request. use when you need to remove all associated emails and results for a specific batch after confirming that the batch data is no longer required. |
| `BOUNCER_DELETE_TOXICITY_LIST_JOB` | Delete Toxicity List Job | Tool to delete a specific toxicity list job. use when you need to remove a completed or unwanted toxicity analysis job after confirming its id. |
| `BOUNCER_FINISH_BATCH` | Finish Batch | Tool to mark a batch verification process as finished. use after batch processing completes to stop further verifications and reclaim unused credits. |
| `BOUNCER_GET_BATCH_RESULTS` | Get Batch Results | Tool to retrieve the results of a batch verification process. use after submitting a batch to fetch all processed email verification outcomes. |
| `BOUNCER_VERIFY_DOMAIN` | Verify Domain | Tool to verify the validity and configuration of a domain. use when you need to confirm the domain's mx records and catch-all behavior. |
| `BOUNCER_VERIFY_EMAIL` | Verify Email | Tool to verify a single email address in real-time. use when validating email entry form inputs instantly. |

## Supported Triggers

None listed.

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

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

url = session.mcp.url
```

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

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

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

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

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

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

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

## Related Toolkits

- [Apilio](https://composio.dev/toolkits/apilio) - Apilio is a home automation platform that lets you connect and control smart devices from different brands. It helps you build flexible automations with complex conditions, schedules, and integrations.
- [Basin](https://composio.dev/toolkits/basin) - Basin is a no-code form backend for quickly setting up reliable contact forms. It lets you collect and manage form submissions without writing any server-side code.
- [Conveyor](https://composio.dev/toolkits/conveyor) - Conveyor is a platform that automates security reviews with a Trust Center and AI-driven questionnaire automation. It streamlines compliance and vendor security processes for faster, hassle-free reviews.
- [Crowdin](https://composio.dev/toolkits/crowdin) - Crowdin is a localization management platform that streamlines translation workflows and collaboration. It helps teams centralize multilingual content, boost productivity, and automate translation processes.
- [Databox](https://composio.dev/toolkits/databox) - Databox is a business analytics platform that connects your data from any tool and device. It helps you track KPIs, build dashboards, and discover actionable insights.
- [Detrack](https://composio.dev/toolkits/detrack) - Detrack is a delivery management platform for real-time tracking and proof of delivery. It helps businesses automate notifications and keep customers updated every step of the way.
- [Dnsfilter](https://composio.dev/toolkits/dnsfilter) - Dnsfilter is a cloud-based DNS security and content filtering solution. It helps organizations block online threats and manage safe internet access with ease.
- [Faraday](https://composio.dev/toolkits/faraday) - Faraday lets you embed AI in workflows across your stack for smarter automation. It boosts your favorite tools with actionable intelligence and seamless integration.
- [Feathery](https://composio.dev/toolkits/feathery) - Feathery is an AI-powered platform for building dynamic data intake forms with advanced logic. It helps teams automate complex workflows and collect structured data with ease.
- [Fillout forms](https://composio.dev/toolkits/fillout_forms) - Fillout forms is an online platform for building and managing forms with a flexible API. It lets you create, distribute, and collect responses from forms with ease.
- [Formdesk](https://composio.dev/toolkits/formdesk) - Formdesk is an online form builder for creating and managing professional forms. It's perfect for collecting data, automating workflows, and integrating form submissions with your favorite services.
- [Formsite](https://composio.dev/toolkits/formsite) - Formsite lets you build online forms and surveys with drag-and-drop simplicity. Capture, manage, and integrate form responses securely for streamlined workflows.
- [Graphhopper](https://composio.dev/toolkits/graphhopper) - GraphHopper is an enterprise-grade Directions API for routing, optimization, and geocoding across multiple vehicle types. It enables fast, reliable route planning and logistics automation for businesses.
- [Hyperbrowser](https://composio.dev/toolkits/hyperbrowser) - Hyperbrowser is a next-generation platform for scalable browser automation. It empowers AI agents to interact with web apps, automate workflows, and handle browser sessions at scale.
- [La Growth Machine](https://composio.dev/toolkits/lagrowthmachine) - La Growth Machine automates multi-channel sales outreach and routine tasks for sales teams. Streamline your workflow and focus on closing more deals.
- [Leverly](https://composio.dev/toolkits/leverly) - Leverly is a workflow automation platform that connects and coordinates actions across your apps. It streamlines repetitive processes so your business runs smoother, faster, and with fewer manual steps.
- [Maintainx](https://composio.dev/toolkits/maintainx) - Maintainx is a cloud-based CMMS for centralizing maintenance data, communication, and workflows. It helps organizations streamline maintenance operations and improve team coordination.
- [Make](https://composio.dev/toolkits/make) - Make is an automation platform that connects your favorite apps and services. Build powerful, custom workflows without writing code.
- [Ntfy](https://composio.dev/toolkits/ntfy) - Ntfy is a notification service to send push messages to phones or desktops. Instantly deliver alerts and updates to users, devices, or teams.
- [Persona](https://composio.dev/toolkits/persona) - Persona offers identity infrastructure to automate user verification and compliance. It helps organizations securely verify users and reduce fraud risk.

## Frequently Asked Questions

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

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

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

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

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