How to integrate Hunter MCP with Autogen

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Introduction

This guide walks you through connecting Hunter to AutoGen using the Composio tool router. By the end, you'll have a working Hunter agent that can find all public emails at acme.com, enrich company details for tesla.com, create new lead with given info, delete a lead by their id through natural language commands.

This guide will help you understand how to give your AutoGen agent real control over a Hunter account through Composio's Hunter MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

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 Hunter
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Hunter tools
  • Run a live chat loop where you ask the agent to perform Hunter 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 Hunter MCP server, and what's possible with it?

The Hunter MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Hunter account. It provides structured and secure access to your lead generation and enrichment tools, so your agent can perform actions like finding emails, enriching company data, managing leads, and organizing leads lists on your behalf.

  • Email discovery and search: Instantly ask your agent to find all public email addresses for a given company or domain, complete with metadata to fuel your outreach and marketing campaigns.
  • Smart lead creation and management: Let your agent add new leads, update lead details, or delete outdated entries to keep your Hunter account organized and up-to-date.
  • Company and contact enrichment: Have the agent fetch detailed company profiles or use the Email Finder to infer the best contact email for a specific person at a target company.
  • Leads list organization: Direct your agent to create, update, or remove custom leads lists—making it easy to segment prospects for personalized marketing or sales workflows.
  • Custom attribute management: Empower your agent to create or delete custom lead attributes, tailoring your CRM data fields to match your unique business needs.

Supported Tools & Triggers

Tools
Account InformationTool to retrieve information about your hunter account.
Company EnrichmentTool to get enrichment information for a company by its domain.
Create custom lead attributeTool to create a new custom lead attribute in your account.
Create LeadTool to create a new lead.
Create Leads ListTool to create a new leads list.
Delete Custom AttributeTool to delete an existing custom attribute.
Delete LeadTool to delete a lead.
Delete Leads ListTool to delete a leads list by its id.
Domain SearchTool to search all email addresses for a given domain or company.
Email FinderTool to find the most likely email address for a person at a domain or company.
Email VerifierTool to verify the deliverability of an email address.
Get Custom AttributeTool to retrieve details of a specific custom attribute.
Get LeadTool to retrieve details of a specific lead by id.
Get Leads ListTool to retrieve details of a specific leads list by id.
List Custom AttributesTool to list all custom lead attributes in your account.
List LeadsTool to list all leads saved in your account with optional filters.
List Leads ListsTool to list all leads lists in your account.
Update Custom AttributeTool to update an existing custom attribute's label.
Update LeadTool to update details of an existing lead by id.
Update Leads ListTool to update the name of a specific leads list.
Upsert LeadTool to create or update a lead by email in one call.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Hunter account you can connect to Composio
  • Some basic familiarity with Autogen and Python async

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard 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.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install dependencies

bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Hunter via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

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 Hunter connections to use

Import dependencies and create Tool Router session

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 Hunter session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["hunter"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Hunter tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to

Configure MCP parameters for Autogen

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")}
)

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

Create the model client and agent

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 Hunter assistant agent with MCP tools
    agent = AssistantAgent(
        name="hunter_assistant",
        description="An AI assistant that helps with Hunter operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Hunter tools from the workbench

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Hunter 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")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Hunter 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

Complete Code

Here's the complete code to get you started with Hunter and AutoGen:

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 Hunter session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["hunter"]
    )
    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 Hunter assistant agent with MCP tools
        agent = AssistantAgent(
            name="hunter_assistant",
            description="An AI assistant that helps with Hunter 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 Hunter 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 Hunter 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 Hunter, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

How to build Hunter MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Hunter MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Hunter MCP with Claude Agent SDK

Google ADK

Google ADK

Use Hunter MCP with Google ADK

LangChain

LangChain

Use Hunter MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Hunter MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Hunter MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Hunter MCP with LlamaIndex

CrewAI

CrewAI

Use Hunter MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Hunter MCP?

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

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

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

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