# How to integrate Kickbox MCP with Pydantic AI

```json
{
  "title": "How to integrate Kickbox MCP with Pydantic AI",
  "toolkit": "Kickbox",
  "toolkit_slug": "kickbox",
  "framework": "Pydantic AI",
  "framework_slug": "pydantic-ai",
  "url": "https://composio.dev/toolkits/kickbox/framework/pydantic-ai",
  "markdown_url": "https://composio.dev/toolkits/kickbox/framework/pydantic-ai.md",
  "updated_at": "2026-05-12T10:16:48.492Z"
}
```

## Introduction

This guide walks you through connecting Kickbox to Pydantic AI using the Composio tool router. By the end, you'll have a working Kickbox agent that can check if this email is valid, verify deliverability of a customer email, validate new sign-up email address through natural language commands.
This guide will help you understand how to give your Pydantic AI agent real control over a Kickbox account through Composio's Kickbox MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Kickbox with

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

## 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 Kickbox
- 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 Kickbox 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 Kickbox MCP server, and what's possible with it?

The Kickbox MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Kickbox account. It provides structured and secure access to real-time email verification and quality scoring, so your agent can instantly validate email addresses, reduce bounce rates, and maintain clean contact lists on your behalf.
- Instant single email verification: Ask your agent to check if an email address is valid and deliverable before sending important communications.
- Preventing invalid email submissions: Ensure only legitimate and active emails are accepted into your systems, reducing bounce risk and protecting sender reputation.
- Real-time workflow integration: Validate emails as part of onboarding, registration, or transactional processes to catch errors and typos immediately.
- Email quality scoring: Assess the likelihood of deliverability and filter out risky or low-quality addresses from your lists.
- Automated contact list hygiene: Regularly verify individual emails to keep your CRM and marketing lists accurate and up to date.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `KICKBOX_CHECK_DISPOSABLE` | Check Disposable Email | Tool to check if an email address or domain is from a disposable email service. Use when you need to validate whether an email is temporary or throwaway (e.g., mailinator.com, trashmail.com). This is a free public endpoint that does not require authentication. |
| `KICKBOX_KICKBOX_SINGLE_VERIFICATION_API` | Kickbox Single Verification | Tool to verify a single email address via Kickbox. Use when you need real-time validation of an individual email before critical workflows. |

## Supported Triggers

None listed.

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

The Kickbox MCP server is an implementation of the Model Context Protocol that connects your AI agent to Kickbox. It provides structured and secure access so your agent can perform Kickbox 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

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

### 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 the required libraries.
What's happening:
- composio connects your agent to external SaaS tools like Kickbox
- pydantic-ai lets you create structured AI agents with tool support
- python-dotenv loads your environment variables securely from a .env file
```bash
pip install composio pydantic-ai python-dotenv
```

### 3. Set up environment variables

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
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key
```

### 4. Import dependencies

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

### 5. Create a Tool Router Session

What's happening:
- We're creating a Tool Router session that gives your agent access to Kickbox 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
```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 Kickbox
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["kickbox"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
```

### 6. Initialize the Pydantic AI Agent

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

### 7. Build the chat interface

What's happening:
- The agent reads input from the terminal and streams its response
- Kickbox API calls happen automatically under the hood
- The model keeps conversation history to maintain context across turns
```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 Kickbox.\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()
```

### 8. Run the application

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

## Complete Code

```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()

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 Kickbox
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["kickbox"],
    )
    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
    kickbox_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[kickbox_mcp],
        instructions=(
            "You are a Kickbox assistant. Use Kickbox 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 Kickbox.\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 Kickbox through Composio's Tool Router. With this setup, your agent can perform real Kickbox 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 + Kickbox 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 Kickbox MCP Agent with another framework

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

## Related Toolkits

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- [Ahrefs](https://composio.dev/toolkits/ahrefs) - Ahrefs is an SEO and marketing platform for site audits, keyword research, and competitor insights. It helps you improve search rankings and drive organic traffic.
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- [Brandfetch](https://composio.dev/toolkits/brandfetch) - Brandfetch is an API that delivers company logos, colors, and visual branding assets. It helps marketers and developers keep brand visuals consistent everywhere.
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- [Campayn](https://composio.dev/toolkits/campayn) - Campayn is an email marketing platform for creating, sending, and managing campaigns. It helps businesses engage contacts and grow audiences with easy-to-use tools.
- [Cardly](https://composio.dev/toolkits/cardly) - Cardly is a platform for creating and sending personalized direct mail to customers. It helps businesses break through the digital clutter by getting real engagement via physical mailboxes.
- [ClickSend](https://composio.dev/toolkits/clicksend) - ClickSend is a cloud-based SMS and email marketing platform for businesses. It streamlines communication by enabling quick message delivery and contact management.
- [Crustdata](https://composio.dev/toolkits/crustdata) - CrustData is an AI-powered data intelligence platform for real-time company and people data. It helps B2B sales teams, AI SDRs, and investors react to live business signals.
- [Curated](https://composio.dev/toolkits/curated) - Curated is a platform for collecting, curating, and publishing newsletters. It streamlines content aggregation and distribution for creators and teams.
- [Customerio](https://composio.dev/toolkits/customerio) - Customer.io is a customer engagement platform for targeted messaging across email, SMS, and push. Easily automate, segment, and track communications with your audience.
- [Cutt ly](https://composio.dev/toolkits/cutt_ly) - Cutt.ly is a URL shortening service for managing and analyzing links. Streamline your workflows with quick, trackable, and branded short URLs.
- [Demio](https://composio.dev/toolkits/demio) - Demio is webinar software built for marketers, offering both live and automated sessions with interactive features. It helps teams engage audiences and optimize lead generation through detailed analytics.

## Frequently Asked Questions

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

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

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

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

---
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