# How to integrate Tomba MCP with LangChain

```json
{
  "title": "How to integrate Tomba MCP with LangChain",
  "toolkit": "Tomba",
  "toolkit_slug": "tomba",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/tomba/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/tomba/framework/langchain.md",
  "updated_at": "2026-05-12T10:28:53.541Z"
}
```

## Introduction

This guide walks you through connecting Tomba to LangChain using the Composio tool router. By the end, you'll have a working Tomba agent that can find all leads from example.com domain, check if this email is disposable, list your current api keys in tomba through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Tomba account through Composio's Tomba MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Tomba with

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

## TL;DR

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

The Tomba MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Tomba account. It provides structured and secure access to your B2B email finding, lead management, and account configuration tools, so your agent can perform actions like discovering leads, managing lists, validating domains, and monitoring account usage on your behalf.
- Lead discovery and enrichment: Ask your agent to list available lead attributes or add new leads directly into your Tomba account for streamlined outreach.
- Lead list management: Effortlessly retrieve, update, or delete lead lists, helping you stay organized and keep your data current.
- Domain validation and status checks: Have your agent check if a domain is webmail or disposable to ensure better deliverability and lead quality.
- API key and account management: Direct your agent to list, create, or revoke API keys, and review usage statistics to keep your Tomba integration secure and efficient.
- Usage monitoring and reporting: Let your agent fetch up-to-date API usage statistics, keeping you informed about your plan limits and consumption.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `TOMBA_ATTRIBUTES_LIST` | List Lead Attributes | Retrieves all custom lead attributes defined in your Tomba account. Use this action to discover the available attributes that can be used when creating or updating leads. Returns attribute metadata including name, identifier, type, and timestamps. No input parameters required. |
| `TOMBA_DOMAIN_STATUS` | Domain Status | Tool to check if a domain is webmail or disposable. Use when validating email deliverability constraints. |
| `TOMBA_KEYS_DELETE` | Delete API Key by ID | Tool to delete an API key by its numeric ID. Use when you need to permanently revoke an API key before its expiration. Note: You can get the numeric key ID from the TOMBA_KEYS_LIST action. |
| `TOMBA_KEYS_LIST` | List API Keys | Tool to list all API keys. Use when you want to retrieve information about your existing Tomba API keys. |
| `TOMBA_LEADS_CREATE` | Create Lead | Create a new lead in Tomba's lead database. Use this to store contact information for a person you want to track. Returns the unique ID of the created lead. Required fields: first_name, email. All other fields are optional. |
| `TOMBA_LEADS_LIST` | List Leads | Tool to list all leads. Use when you need to retrieve and paginate your leads list. |
| `TOMBA_LISTS_DELETE` | Delete Leads List by ID | Tool to delete a leads list by ID. Use when you need to permanently remove a list after confirming its ID. |
| `TOMBA_LISTS_LIST` | List Lead Lists | Tool to list all lead lists. Use when you need to retrieve and paginate your lead lists. |
| `TOMBA_LISTS_UPDATE` | Update Leads List | Tool to update a leads list's name by ID. Use when renaming an existing list after obtaining its ID. |
| `TOMBA_USAGE_STATS` | Get Usage Statistics | Tool to get API usage statistics. Use when you need to monitor account usage and avoid hitting limits. |

## Supported Triggers

None listed.

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

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

url = session.mcp.url
```

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

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

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

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

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

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

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

## Related Toolkits

- [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.
- [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.
- [Clearout](https://composio.dev/toolkits/clearout) - Clearout is an AI-powered service for verifying, finding, and enriching email addresses. It boosts deliverability and helps you discover high-quality leads effortlessly.
- [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.
- [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.
- [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.
- [Gleap](https://composio.dev/toolkits/gleap) - Gleap is an all-in-one customer feedback tool for apps and websites. It helps you understand user pain points and improve software through direct, actionable insights.
- [Gorgias](https://composio.dev/toolkits/gorgias) - Gorgias is a helpdesk and live chat platform built for e-commerce brands. It helps automate support, manage orders, and unify customer communication across channels.
- [Handwrytten](https://composio.dev/toolkits/handwrytten) - Handwrytten automates handwritten cards and notes using robotic penmanship. Save time while adding a personal touch to your customer or team communications.
- [Helpdesk](https://composio.dev/toolkits/helpdesk) - HelpDesk is a ticketing platform that organizes and manages customer support inquiries. It helps teams streamline their support workflows and respond to customers efficiently.
- [Helpwise](https://composio.dev/toolkits/helpwise) - Helpwise is a unified customer service platform for managing all your business emails, chats, and SMS in one place. It helps teams collaborate on customer conversations more efficiently and never miss important messages.

## Frequently Asked Questions

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

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

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

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

---
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