How to integrate Big data cloud MCP with Pydantic AI

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Introduction

This guide walks you through connecting Big data cloud to Pydantic AI using the Composio tool router. By the end, you'll have a working Big data cloud agent that can check if this ip address is currently roaming, verify if an email address is valid, get country and demographic info for a given ip, fetch cybersecurity hazard report for this ip through natural language commands.

This guide will help you understand how to give your Pydantic AI 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.

TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Big data cloud
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Big data cloud workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

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.

Supported Tools & Triggers

Tools
Am I Roaming APITool to determine if the user is roaming based on their ip address and gps coordinates.
ASN Extended Receiving From Info APITool to return upstream providers (receivingfrom) for a given asn.
ASN Extended Transit To Info APITool to return downstream customers (transitto) for a given asn.
ASN Rank List APITool to fetch a ranked list of autonomous systems by ipv4 announcement volumes.
BGP Active Prefixes APITool to retrieve ipv4 or ipv6 prefixes currently announced on bgp.
Reverse Geocoding With Timezone APITool to return reverse geocoding and time zone info for given coordinates.
Country by IP Address APITool to geolocate an ip address and retrieve country details and demographics.
Country Info APITool to fetch detailed country information by iso code.
Email Address Verification APITool to verify email addresses for syntax, domain validity, and disposability.
Hazard Report APITool to fetch a cybersecurity hazard report for a specified ip address.
Networks by CIDRTool to retrieve bgp-announced networks within a specified cidr range.
Network by IP Address APITool to retrieve registry, asn, and bgp details for a given ip address’s network.
Phone Number Validation by IPTool to validate phone numbers by inferring country from client ip.
Time Zone by IP Address APITool to retrieve time zone information for a given ip address.
Tor Exit Nodes Geolocated APITool to list active tor exit nodes geolocated by country with carrier info.
User Agent Parser APITool to parse a user-agent string into device, os, browser, and bot details.
User Risk APITool to return a risk assessment for a user based on ip signals for fraud prevention.

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

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming

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 pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Big data cloud
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Big data cloud
  • MCPServerStreamableHTTP connects to the Big data cloud MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Big data cloud
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["big_data_cloud"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Big data cloud 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

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
big_data_cloud_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[big_data_cloud_mcp],
    instructions=(
        "You are a Big data cloud assistant. Use Big data cloud tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Big data cloud endpoint
  • The agent uses GPT-5 to interpret user commands and perform Big data cloud operations
  • The instructions field defines the agent's role and behavior

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Big data cloud.\n")

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", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Big data cloud API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Big data cloud and Pydantic AI:

import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Big data cloud
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["big_data_cloud"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    big_data_cloud_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[big_data_cloud_mcp],
        instructions=(
            "You are a Big data cloud assistant. Use Big data cloud tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Big data cloud.\n")

    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", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

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

Conclusion

You've built a Pydantic AI agent that can interact with Big data cloud through Composio's Tool Router. With this setup, your agent can perform real Big data cloud actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Big data cloud for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

How to build Big data cloud MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Big data cloud MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Big data cloud MCP with Claude Agent SDK

Google ADK

Google ADK

Use Big data cloud MCP with Google ADK

LangChain

LangChain

Use Big data cloud MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Big data cloud MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Big data cloud MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Big data cloud MCP with LlamaIndex

CrewAI

CrewAI

Use Big data cloud MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Big data cloud MCP?

With a standalone Big data cloud MCP server, the agents and LLMs can only access a fixed set of Big data cloud tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Big data cloud and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with Pydantic AI?

Yes, you can. Pydantic AI 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 Big data cloud tools.

Can I manage the permissions and scopes for Big data cloud while using Tool Router?

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

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