# How to integrate Tomba MCP with Autogen

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

## Introduction

This guide walks you through connecting Tomba to AutoGen 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 AutoGen 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)
- [LangChain](https://composio.dev/toolkits/tomba/framework/langchain)
- [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
- Install the required dependencies for Autogen and Composio
- Initialize Composio and create a Tool Router session for Tomba
- Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
- Configure an Autogen AssistantAgent that can call Tomba tools
- Run a live chat loop where you ask the agent to perform Tomba operations

## What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.
Key features include:
- Multi-Agent Systems: Build collaborative agent workflows
- MCP Workbench: Native support for Model Context Protocol tools
- Streaming HTTP: Connect to external services through streamable HTTP
- AssistantAgent: Pre-built agent class for tool-using assistants

## 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 agents and assistants directly to Tomba. Instead of manually wiring Tomba APIs, OAuth, and scopes yourself, you get a structured, tool-based interface that an LLM can call safely.
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

You will need:
- A Composio API key
- An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
- A Tomba account you can connect to Composio
- Some basic familiarity with Autogen and Python async

### 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

Install Composio, Autogen extensions, and dotenv.
What's happening:
- composio connects your agent to Tomba via MCP
- autogen-agentchat provides the AssistantAgent class
- autogen-ext-openai provides the OpenAI model client
- autogen-ext-tools provides MCP workbench support
```bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools
```

### 3. Set up environment variables

Create a .env file in your project folder.
What's happening:
- COMPOSIO_API_KEY is required to talk to Composio
- OPENAI_API_KEY is used by Autogen's OpenAI client
- USER_ID is how Composio identifies which user's Tomba connections to use
```bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com
```

### 4. Import dependencies and create Tool Router session

What's happening:
- load_dotenv() reads your .env file
- Composio(api_key=...) initializes the SDK
- create(...) creates a Tool Router session that exposes Tomba tools
- session.mcp.url is the MCP endpoint that Autogen will connect to
```python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Tomba session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["tomba"]
    )
    url = session.mcp.url
```

### 5. Configure MCP parameters for Autogen

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.
What's happening:
- url points to the Tool Router MCP endpoint from Composio
- timeout is the HTTP timeout for requests
- sse_read_timeout controls how long to wait when streaming responses
- terminate_on_close=True cleans up the MCP server process when the workbench is closed
```python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)
```

### 6. Create the model client and agent

What's happening:
- OpenAIChatCompletionClient wraps the OpenAI model for Autogen
- McpWorkbench connects the agent to the MCP tools
- AssistantAgent is configured with the Tomba tools from the workbench
```python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Tomba assistant agent with MCP tools
    agent = AssistantAgent(
        name="tomba_assistant",
        description="An AI assistant that helps with Tomba operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )
```

### 7. Run the interactive chat loop

What's happening:
- The script prompts you in a loop with You:
- Autogen passes your input to the model, which decides which Tomba tools to call via MCP
- agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
- Typing exit, quit, or bye ends the loop
```python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Tomba related question or task to the agent.\n")

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

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

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
```

## Complete Code

```python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Tomba session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["tomba"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Tomba assistant agent with MCP tools
        agent = AssistantAgent(
            name="tomba_assistant",
            description="An AI assistant that helps with Tomba operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

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

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

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

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

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

## Conclusion

You now have an Autogen assistant wired into Tomba through Composio's Tool Router and MCP. From here you can:
- Add more toolkits to the toolkits list, for example notion or hubspot
- Refine the agent description to point it at specific workflows
- Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Tomba, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

## 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)
- [LangChain](https://composio.dev/toolkits/tomba/framework/langchain)
- [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

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- [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.
- [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.
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## 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 Autogen?

Yes, you can. Autogen 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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