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How to integrate Big data cloud MCP with Autogen

Connect Autogen to Big data cloud MCP. Check if this ip address is currently roaming, verify if an email address is valid, and more using natural language, with authentication handled for you.

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BigDataCloud provides APIs for geolocation, reverse geocoding, and address validation. Instantly access reliable location intelligence to enhance your applications and workflows.

17 Tools

Introduction

This guide walks you through connecting Big data cloud to AutoGen using the Composio tool router. By the end, you'll have a working Big data cloud agent that can geolocate IP addresses, reverse geocode coordinates, verify emails and phone numbers, assess user risk, and parse user agents through natural language commands.

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

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

For exact tool slugs and parameter tables, see the tool catalog in the Composio docs.

Also integrate Big data cloud with

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

The Big data cloud MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Big data cloud account. It provides structured and secure access to advanced geolocation, reverse geocoding, ASN analysis, and data validation APIs, so your agent can perform actions like looking up IP details, verifying emails, assessing network risk, and analyzing BGP routing on your behalf.

  • IP geolocation and country insights: Let your agent instantly geolocate any IP address, retrieve country-level demographics, and pull rich metadata about locations worldwide.
  • Reverse geocoding with timezone detection: Have your agent translate GPS coordinates into precise locality information along with accurate timezone data—all in one go.
  • Email address verification and data hygiene: Ensure your agent can validate email addresses for proper syntax, domain legitimacy, and disposability to help maintain clean and reliable datasets.
  • ASN and BGP analytics: Allow your agent to analyze internet routing by fetching ranked lists of autonomous systems, upstream and downstream provider details, and active BGP prefixes for a given ASN.
  • Cybersecurity hazard assessment: Empower your agent to fetch and interpret hazard reports for IP addresses, identifying threats like VPN/proxy usage, blacklist status, and hosting risks.

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

What is Composio SDK?

Composio's Composio SDK 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 Composio SDK

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

How the Composio SDK works

The Composio SDK 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

Step by step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Big data cloud account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
2

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.
3

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 Big data cloud via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

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 Big data cloud connections to use
5

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 Big data cloud session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["big_data_cloud"]
    )
    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 Big data cloud tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

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
7

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 Big data cloud assistant agent with MCP tools
    agent = AssistantAgent(
        name="big_data_cloud_assistant",
        description="An AI assistant that helps with Big data cloud 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 Big data cloud tools from the workbench
8

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Big data cloud 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 Big data cloud 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 Big data cloud 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 Big data cloud session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["big_data_cloud"]
    )
    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 Big data cloud assistant agent with MCP tools
        agent = AssistantAgent(
            name="big_data_cloud_assistant",
            description="An AI assistant that helps with Big data cloud 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 Big data cloud 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 Big data cloud 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 Big data cloud, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
TOOLS

Supported Tools

Every Big data cloud action and event your agent gets out of the box.

Am I Roaming API

Tool to determine if the user is roaming based on their IP address and GPS coordinates.

ASN Extended Receiving From Info API

Tool to return upstream providers (receivingFrom) for a given ASN.

ASN Extended Transit To Info API

Tool to return downstream customers (transitTo) for a given ASN.

ASN Rank List API

Retrieves a paginated list of ranked Autonomous Systems with IPv4 announcement counts.

BGP Active Prefixes API

Tool to retrieve IPv4 or IPv6 prefixes currently announced on BGP.

Country by IP Address API

Tool to geolocate an IP address and retrieve country details and demographics.

Country Info API

Tool to fetch detailed country information by ISO code.

Email Address Verification API

Tool to verify email addresses for syntax, domain validity, and disposability.

Hazard Report API

Tool to fetch a cybersecurity hazard report for a specified IP address.

Networks by CIDR

Tool to retrieve BGP-announced networks within a specified CIDR range.

Network by IP Address API

Tool to retrieve registry, ASN, and BGP details for a given IP address’s network.

Phone Number Validation by IP

Tool to validate phone numbers by inferring country from client IP.

Reverse Geocoding With Timezone API

Tool to return reverse geocoding and time zone info for given coordinates.

Time Zone by IP Address API

Tool to retrieve time zone information for a given IP address.

Tor Exit Nodes Geolocated API

Retrieve a paginated list of active TOR exit node IP addresses with geolocation and carrier (ASN) details.

User Agent Parser API

Tool to parse a User-Agent string into device, OS, browser, and bot details.

User Risk API

Tool to return a risk assessment for a user based on IP signals for fraud prevention.

FRAMEWORKS

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Pydantic AI logo

Pydantic AI

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FAQ

Frequently asked questions

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