# How to integrate Xata MCP with Autogen ```json { "title": "How to integrate Xata MCP with Autogen", "toolkit": "Xata", "toolkit_slug": "xata", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/xata/framework/autogen", "markdown_url": "https://composio.dev/toolkits/xata/framework/autogen.md", "updated_at": "2026-03-29T06:55:54.468Z" } ``` ## Introduction This guide walks you through connecting Xata to AutoGen using the Composio tool router. By the end, you'll have a working Xata agent that can list all records from the customers table, insert a new order with status pending, update the email field for customer 101 through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Xata account through Composio's Xata MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Xata with - [OpenAI Agents SDK](https://composio.dev/toolkits/xata/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/xata/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/xata/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/xata/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/xata/framework/codex) - [OpenClaw](https://composio.dev/toolkits/xata/framework/openclaw) - [Hermes](https://composio.dev/toolkits/xata/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/xata/framework/cli) - [Google ADK](https://composio.dev/toolkits/xata/framework/google-adk) - [LangChain](https://composio.dev/toolkits/xata/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/xata/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/xata/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/xata/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/xata/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 Xata - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Xata tools - Run a live chat loop where you ask the agent to perform Xata 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 Xata MCP server, and what's possible with it? The Xata MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Xata account. It provides structured and secure access so your agent can perform Xata operations on your behalf. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `XATA_GET_ORGANIZATION` | Get Organization Details | Tool to retrieve detailed information about a specific organization by its ID. Use when you need to check organization status, name, billing information, or admin settings. | | `XATA_GET_PROJECT_LIMITS` | Get project resource limits | Tool to retrieve the default resource limits for projects in a specified organization. Use when you need to check maximum instances, storage, allowed regions, or branch limits for a Xata organization. | | `XATA_LIST_EXTENSIONS` | List Extensions | Tool to get available PostgreSQL extensions for a specific image. Use when you need to check which extensions can be enabled for a particular PostgreSQL version in an organization. | | `XATA_LIST_IMAGES` | List Available Images | Tool to retrieve a list of all available postgres images for a specified organization and region. Use when you need to check which database images are available for deployment. | | `XATA_LIST_INSTANCE_TYPES` | List Instance Types | Tool to retrieve a list of all instance types for the specified organization and region. Use when you need to check available instance types and their configurations including CPU, RAM, and pricing. | | `XATA_LIST_ORGANIZATION_API_KEYS` | List Organization API Keys | Tool to retrieve a list of API keys for a specific Xata organization. Use when you need to view all API keys associated with an organization. | | `XATA_LIST_AVAILABLE_REGIONS` | List Available Regions | Tool to retrieve a list of all regions where new branches can be deployed for the specified organization. Use when you need to check which regions are available for deploying new branches. | | `XATA_UPDATE_ORGANIZATION` | Update Organization | Tool to update information for an existing organization, such as its name. Use when you need to modify organization details. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Xata MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Xata. Instead of manually wiring Xata 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 Xata 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 Xata 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 Xata 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 Xata 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 Xata session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["xata"] ) 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 Xata 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 Xata assistant agent with MCP tools agent = AssistantAgent( name="xata_assistant", description="An AI assistant that helps with Xata 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 Xata 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 Xata 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 Xata session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["xata"] ) 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 Xata assistant agent with MCP tools agent = AssistantAgent( name="xata_assistant", description="An AI assistant that helps with Xata 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 Xata 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 Xata 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 Xata, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Xata MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/xata/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/xata/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/xata/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/xata/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/xata/framework/codex) - [OpenClaw](https://composio.dev/toolkits/xata/framework/openclaw) - [Hermes](https://composio.dev/toolkits/xata/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/xata/framework/cli) - [Google ADK](https://composio.dev/toolkits/xata/framework/google-adk) - [LangChain](https://composio.dev/toolkits/xata/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/xata/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/xata/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/xata/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/xata/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. - [Supabase](https://composio.dev/toolkits/supabase) - Supabase is an open-source backend platform offering scalable Postgres databases, authentication, storage, and real-time APIs. It lets developers build modern apps without managing infrastructure. - [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. - [Codeinterpreter](https://composio.dev/toolkits/codeinterpreter) - Codeinterpreter is a Python-based coding environment with built-in data analysis and visualization. It lets you instantly run scripts, plot results, and prototype solutions inside supported platforms. - [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. - [GitHub](https://composio.dev/toolkits/github) - GitHub is a code hosting platform for version control and collaborative software development. It streamlines project management, code review, and team workflows in one place. - [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. - [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. - [Excel](https://composio.dev/toolkits/excel) - Microsoft Excel is a robust spreadsheet application for organizing, analyzing, and visualizing data. It's the go-to tool for calculations, reporting, and flexible data management. - [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. - [21risk](https://composio.dev/toolkits/_21risk) - 21RISK is a web app built for easy checklist, audit, and compliance management. It streamlines risk processes so teams can focus on what matters. - [Ably](https://composio.dev/toolkits/ably) - Ably is a real-time messaging platform for live chat and data sync in modern apps. It offers global scale and rock-solid reliability for seamless, instant experiences. - [Abstract](https://composio.dev/toolkits/abstract) - Abstract provides a suite of APIs for automating data validation and enrichment tasks. It helps developers streamline workflows and ensure data quality with minimal effort. - [Abuselpdb](https://composio.dev/toolkits/abuselpdb) - Abuselpdb is a central database for reporting and checking IPs linked to malicious online activity. Use it to quickly identify and report suspicious or abusive IP addresses. - [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. - [Agentql](https://composio.dev/toolkits/agentql) - Agentql is a toolkit that connects AI agents to the web using a specialized query language. It enables structured web interaction and data extraction for smarter automations. - [Agenty](https://composio.dev/toolkits/agenty) - Agenty is a web scraping and automation platform for extracting data and automating browser tasks—no coding needed. It streamlines data collection, monitoring, and repetitive online actions. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Xata MCP? With a standalone Xata MCP server, the agents and LLMs can only access a fixed set of Xata tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Xata 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 Xata tools. ### Can I manage the permissions and scopes for Xata while using Tool Router? Yes, absolutely. You can configure which Xata 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 Xata data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)