# How to integrate Databox MCP with Pydantic AI ```json { "title": "How to integrate Databox MCP with Pydantic AI", "toolkit": "Databox", "toolkit_slug": "databox", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/databox/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/databox/framework/pydantic-ai.md", "updated_at": "2026-03-29T06:29:38.850Z" } ``` ## Introduction This guide walks you through connecting Databox to Pydantic AI using the Composio tool router. By the end, you'll have a working Databox agent that can get the latest kpi values for q2, generate a weekly trend report for leads, list dashboards with declining performance metrics through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Databox account through Composio's Databox MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Databox with - [OpenAI Agents SDK](https://composio.dev/toolkits/databox/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/databox/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/databox/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/databox/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/databox/framework/codex) - [OpenClaw](https://composio.dev/toolkits/databox/framework/openclaw) - [Hermes](https://composio.dev/toolkits/databox/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/databox/framework/cli) - [Google ADK](https://composio.dev/toolkits/databox/framework/google-adk) - [LangChain](https://composio.dev/toolkits/databox/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/databox/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/databox/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/databox/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/databox/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 Databox - 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 Databox 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 Databox MCP server, and what's possible with it? The Databox MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Databox account. It provides structured and secure access so your agent can perform Databox operations on your behalf. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `DATABOX_CREATE_DATASET` | Create Dataset | Tool to create a new dataset in Databox data source. Use when you need to initialize a dataset with a title, data source ID, and primary keys for unique record identification. | | `DATABOX_CREATE_DATA_SOURCE` | Create Data Source | Tool to create a new data source in Databox. Use when you need to create a logical container for datasets within a Databox account. Requires accountId, title, and timezone parameters. | | `DATABOX_DELETE_DATASET` | Delete Dataset | Tool to delete a dataset by ID in Databox. Use when you need to permanently remove a dataset. This operation is irreversible. | | `DATABOX_DELETE_DATA_SOURCE` | Delete Data Source | Tool to delete a data source by ID in Databox. Use when you need to permanently remove a data source. This operation is irreversible and will delete all associated datasets. | | `DATABOX_GET_DATASET_INGESTION_STATUS` | Get Dataset Ingestion Status | Tool to check the status of a specific data ingestion for a dataset. Use when you need to verify whether a data ingestion was successful by providing the dataset ID and ingestion ID returned from the initial POST request. | | `DATABOX_LIST_ACCOUNTS` | List Accounts | Tool to retrieve all Databox accounts accessible to the authenticated user. Use to identify account IDs required for subsequent API operations like data source creation. | | `DATABOX_PUSH_DATA_TO_DATASET_V1` | Push Data to Dataset (V1) | Tool to push data points to a Databox dataset using the v1 API. Use when you need to ingest data records into a specific dataset by providing the dataset ID and an array of records matching the dataset schema. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Databox MCP server is an implementation of the Model Context Protocol that connects your AI agent to Databox. It provides structured and secure access so your agent can perform Databox 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 Databox - 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 Databox - MCPServerStreamableHTTP connects to the Databox 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 Databox 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 Databox composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["databox"], ) 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 Databox endpoint - The agent uses GPT-5 to interpret user commands and perform Databox operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent databox_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[databox_mcp], instructions=( "You are a Databox assistant. Use Databox 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 - Databox 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 Databox.\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 Databox composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["databox"], ) 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 databox_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[databox_mcp], instructions=( "You are a Databox assistant. Use Databox 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 Databox.\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 Databox through Composio's Tool Router. With this setup, your agent can perform real Databox 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 + Databox 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 Databox MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/databox/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/databox/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/databox/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/databox/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/databox/framework/codex) - [OpenClaw](https://composio.dev/toolkits/databox/framework/openclaw) - [Hermes](https://composio.dev/toolkits/databox/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/databox/framework/cli) - [Google ADK](https://composio.dev/toolkits/databox/framework/google-adk) - [LangChain](https://composio.dev/toolkits/databox/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/databox/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/databox/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/databox/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/databox/framework/crew-ai) ## Related Toolkits - [Google Sheets](https://composio.dev/toolkits/googlesheets) - Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly. - [Notion](https://composio.dev/toolkits/notion) - Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place. - [Airtable](https://composio.dev/toolkits/airtable) - Airtable combines the flexibility of spreadsheets with the power of a database for easy project and data management. Teams use Airtable to organize, track, and collaborate with custom views and automations. - [Asana](https://composio.dev/toolkits/asana) - Asana is a collaborative work management platform for teams to organize and track projects. It streamlines teamwork, boosts productivity, and keeps everyone aligned on goals. - [Google Tasks](https://composio.dev/toolkits/googletasks) - Google Tasks is a to-do list and task management tool integrated into Gmail and Google Calendar. It helps you organize, track, and complete tasks across your Google ecosystem. - [Linear](https://composio.dev/toolkits/linear) - Linear is a modern issue tracking and project planning tool for fast-moving teams. It helps streamline workflows, organize projects, and boost productivity. - [Firecrawl](https://composio.dev/toolkits/firecrawl) - Firecrawl automates large-scale web crawling and data extraction. It helps organizations efficiently gather, index, and analyze content from online sources. - [Tavily](https://composio.dev/toolkits/tavily) - Tavily offers powerful search and data retrieval from documents, databases, and the web. It helps teams locate and filter information instantly, saving hours on research. - [Jira](https://composio.dev/toolkits/jira) - Jira is Atlassian’s platform for bug tracking, issue tracking, and agile project management. 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It helps you ensure accurate customer and contact data every time. - [Agiled](https://composio.dev/toolkits/agiled) - Agiled is an all-in-one business management platform for CRM, projects, and finance. It helps you streamline workflows, consolidate client data, and manage business processes in one place. - [Amplitude](https://composio.dev/toolkits/amplitude) - Amplitude is a digital analytics platform for product and behavioral data insights. It helps teams analyze user journeys and make data-driven decisions quickly. - [Apilio](https://composio.dev/toolkits/apilio) - Apilio is a home automation platform that lets you connect and control smart devices from different brands. It helps you build flexible automations with complex conditions, schedules, and integrations. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Databox MCP? With a standalone Databox MCP server, the agents and LLMs can only access a fixed set of Databox tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Databox 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 Databox tools. ### Can I manage the permissions and scopes for Databox while using Tool Router? Yes, absolutely. You can configure which Databox 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 Databox data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)