# How to integrate Sendspark MCP with Pydantic AI

```json
{
  "title": "How to integrate Sendspark MCP with Pydantic AI",
  "toolkit": "Sendspark",
  "toolkit_slug": "sendspark",
  "framework": "Pydantic AI",
  "framework_slug": "pydantic-ai",
  "url": "https://composio.dev/toolkits/sendspark/framework/pydantic-ai",
  "markdown_url": "https://composio.dev/toolkits/sendspark/framework/pydantic-ai.md",
  "updated_at": "2026-05-12T10:25:25.127Z"
}
```

## Introduction

This guide walks you through connecting Sendspark to Pydantic AI using the Composio tool router. By the end, you'll have a working Sendspark agent that can add a new prospect to your latest campaign, list all dynamic video campaigns in workspace, fetch prospect data by email for a campaign through natural language commands.
This guide will help you understand how to give your Pydantic AI agent real control over a Sendspark account through Composio's Sendspark MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Sendspark with

- [OpenAI Agents SDK](https://composio.dev/toolkits/sendspark/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/sendspark/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/sendspark/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/sendspark/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/sendspark/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/sendspark/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/sendspark/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/sendspark/framework/cli)
- [Google ADK](https://composio.dev/toolkits/sendspark/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/sendspark/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/sendspark/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/sendspark/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/sendspark/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/sendspark/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 Sendspark
- 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 Sendspark 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 Sendspark MCP server, and what's possible with it?

The Sendspark MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Sendspark account. It provides structured and secure access to your video campaigns and prospect data, so your agent can create campaigns, manage prospects, audit webhooks, and fetch campaign analytics on your behalf.
- Dynamic campaign creation and management: Instantly launch new dynamic video campaigns or fetch details of existing campaigns in your workspace without manual setup.
- Prospect automation at scale: Add individual or multiple prospects to video campaigns, retrieve their details by email, and streamline personalized outreach in seconds.
- Webhook auditing and management: List all configured webhooks or remove outdated ones to keep your integrations secure and up-to-date.
- Campaign analytics and tracking: Retrieve data and performance metrics for your campaigns and prospects to monitor engagement and optimize results.
- API health monitoring: Check Sendspark API health status before making calls, ensuring your automations always run smoothly.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `SENDSPARK_ADD_MULTIPLE_PROSPECTS_TO_DYNAMIC_CAMPAIGN` | Add Multiple Prospects to Dynamic Campaign | Tool to add multiple prospects to a dynamic campaign in bulk. Use when you need to add many prospects to your dynamic video campaign at once after confirming associated charges. |
| `SENDSPARK_ADD_PROSPECT_TO_DYNAMIC_VIDEO_CAMPAIGN` | Add Prospect to Dynamic Video Campaign | Tool to add a prospect to a dynamic video campaign. Use after confirming workspace and campaign IDs. Example: Add new prospect with name/contact details to dynamic "dyn12345" under a known workspace. |
| `SENDSPARK_API_HEALTH_STATUS` | API Health Status | Tool to check the health status of the Sendspark API. Use before making other API calls to ensure the service is up. |
| `SENDSPARK_CREATE_DYNAMIC_VIDEO_CAMPAIGN2` | Create Dynamic Video Campaign V2 | Tool to create a dynamic video campaign in a workspace. Use when you need to create a container for AI-personalized dynamic videos that can be sent to prospects. |
| `SENDSPARK_DELETE_WEBHOOK` | Delete Webhook | Delete a webhook by its unique ID. Returns a structured response with status code and message. This action is idempotent: deleting a non-existent webhook (404) with workspaceId provided returns success. Invalid webhook IDs return 400 with error details. Best practice: Always provide workspaceId to use the reliable workspace-scoped endpoint. |
| `SENDSPARK_GET_DYNAMIC_CAMPAIGN_BY_ID` | Get Dynamic Campaign by ID | Tool to retrieve details of a specific dynamic video campaign. Use after confirming workspace and campaign IDs. |
| `SENDSPARK_GET_WORKSPACE_PROSPECT_DATA_BY_EMAIL` | Get Workspace Prospect Data by Email | Tool to retrieve prospect data by email in a dynamic campaign. Use after adding a prospect to a campaign to fetch its details. |
| `SENDSPARK_LIST_DYNAMIC_VIDEO_CAMPAIGNS` | List Dynamic Video Campaigns | Tool to list all dynamic video campaigns in a workspace. Use when retrieving campaigns with optional pagination, filtering, or search. |
| `SENDSPARK_LIST_WEBHOOKS` | List Webhooks | Retrieves all configured webhooks for a Sendspark workspace. Webhooks are automated notifications sent when specific events occur in dynamic video campaigns (e.g., video created, video played, CTA clicked, video opened). Use this action to audit active webhook configurations, verify webhook URLs, or check which events are being monitored. Returns an empty list if no webhooks are configured. |

## Supported Triggers

None listed.

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

The Sendspark MCP server is an implementation of the Model Context Protocol that connects your AI agent to Sendspark. It provides structured and secure access so your agent can perform Sendspark 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 Sendspark
- 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 Sendspark
- MCPServerStreamableHTTP connects to the Sendspark 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 Sendspark 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 Sendspark
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["sendspark"],
    )
    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 Sendspark endpoint
- The agent uses GPT-5 to interpret user commands and perform Sendspark operations
- The instructions field defines the agent's role and behavior
```python
# Attach the MCP server to a Pydantic AI Agent
sendspark_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[sendspark_mcp],
    instructions=(
        "You are a Sendspark assistant. Use Sendspark 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
- Sendspark 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 Sendspark.\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 Sendspark
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["sendspark"],
    )
    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
    sendspark_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[sendspark_mcp],
        instructions=(
            "You are a Sendspark assistant. Use Sendspark 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 Sendspark.\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 Sendspark through Composio's Tool Router. With this setup, your agent can perform real Sendspark 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 + Sendspark 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 Sendspark MCP Agent with another framework

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

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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.
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## Frequently Asked Questions

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

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

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

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

---
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