# How to integrate Databox MCP with Autogen ```json { "title": "How to integrate Databox MCP with Autogen", "toolkit": "Databox", "toolkit_slug": "databox", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/databox/framework/autogen", "markdown_url": "https://composio.dev/toolkits/databox/framework/autogen.md", "updated_at": "2026-03-29T06:29:38.850Z" } ``` ## Introduction This guide walks you through connecting Databox to AutoGen 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 AutoGen 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: - 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 Databox - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Databox tools - Run a live chat loop where you ask the agent to perform Databox 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 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 agents and assistants directly to Databox. Instead of manually wiring Databox APIs, OAuth, and scopes yourself, you get a structured, tool-based interface that an LLM can call safely. 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 You will need: - A Composio API key - An OpenAI API key (used by Autogen's OpenAIChatCompletionClient) - A Databox account you can connect to Composio - Some basic familiarity with Autogen and Python async ### 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 Composio, Autogen extensions, and dotenv. What's happening: - composio connects your agent to Databox via MCP - autogen-agentchat provides the AssistantAgent class - autogen-ext-openai provides the OpenAI model client - autogen-ext-tools provides MCP workbench support ```bash pip install composio python-dotenv pip install autogen-agentchat autogen-ext-openai autogen-ext-tools ``` ### 3. Set up environment variables 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 Databox connections to use ```bash COMPOSIO_API_KEY=your-composio-api-key OPENAI_API_KEY=your-openai-api-key USER_ID=your-user-identifier@example.com ``` ### 4. Import dependencies and create Tool Router session What's happening: - load_dotenv() reads your .env file - Composio(api_key=...) initializes the SDK - create(...) creates a Tool Router session that exposes Databox tools - session.mcp.url is the MCP endpoint that Autogen will connect to ```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 Databox session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["databox"] ) url = session.mcp.url ``` ### 5. Configure MCP parameters for Autogen 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 ```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")} ) ``` ### 6. Create the model client and agent What's happening: - OpenAIChatCompletionClient wraps the OpenAI model for Autogen - McpWorkbench connects the agent to the MCP tools - AssistantAgent is configured with the Databox tools from the workbench ```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 Databox assistant agent with MCP tools agent = AssistantAgent( name="databox_assistant", description="An AI assistant that helps with Databox operations.", model_client=model_client, workbench=workbench, model_client_stream=True, max_tool_iterations=10 ) ``` ### 7. Run the interactive chat loop What's happening: - The script prompts you in a loop with You: - Autogen passes your input to the model, which decides which Databox 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 ```python print("Chat started! Type 'exit' or 'quit' to end the conversation.\n") print("Ask any Databox 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") ``` ## Complete Code ```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 Databox session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["databox"] ) 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 Databox assistant agent with MCP tools agent = AssistantAgent( name="databox_assistant", description="An AI assistant that helps with Databox 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 Databox 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 Databox 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 Databox, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## 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. It helps teams organize work, prioritize tasks, and deliver projects efficiently. - [Exa](https://composio.dev/toolkits/exa) - Exa is a data extraction and search platform for gathering and analyzing information from websites, APIs, or databases. It helps teams quickly surface insights and automate data-driven workflows. - [Serpapi](https://composio.dev/toolkits/serpapi) - SerpApi is a real-time API for structured search engine results. It lets you automate SERP data collection, parsing, and analysis for SEO and research. - [Clickup](https://composio.dev/toolkits/clickup) - ClickUp is an all-in-one productivity platform for managing tasks, docs, goals, and team collaboration. It streamlines project workflows so teams can work smarter and stay organized in one place. - [Monday](https://composio.dev/toolkits/monday) - Monday.com is a customizable work management platform for project planning and collaboration. It helps teams organize tasks, automate workflows, and track progress in real time. - [Peopledatalabs](https://composio.dev/toolkits/peopledatalabs) - Peopledatalabs delivers B2B data enrichment and identity resolution APIs. Supercharge your apps with accurate, up-to-date business and contact data. - [Snowflake](https://composio.dev/toolkits/snowflake) - Snowflake is a cloud data warehouse built for elastic scaling, secure data sharing, and fast SQL analytics across major clouds. - [Posthog](https://composio.dev/toolkits/posthog) - PostHog is an open-source analytics platform for tracking user interactions and product metrics. It helps teams refine features, analyze funnels, and reduce churn with actionable insights. - [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. 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 Autogen? Yes, you can. Autogen 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)