# How to integrate Stack Ai MCP with Autogen ```json { "title": "How to integrate Stack Ai MCP with Autogen", "toolkit": "Stack Ai", "toolkit_slug": "stack_ai", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/stack_ai/framework/autogen", "markdown_url": "https://composio.dev/toolkits/stack_ai/framework/autogen.md", "updated_at": "2026-03-29T06:51:33.473Z" } ``` ## Introduction This guide walks you through connecting Stack Ai to AutoGen using the Composio tool router. By the end, you'll have a working Stack Ai agent that can list all running workflows in stack ai, trigger the monthly data sync workflow, get status of recent workflow runs through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Stack Ai account through Composio's Stack Ai MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Stack Ai with - [OpenAI Agents SDK](https://composio.dev/toolkits/stack_ai/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/stack_ai/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/stack_ai/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/stack_ai/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/stack_ai/framework/codex) - [OpenClaw](https://composio.dev/toolkits/stack_ai/framework/openclaw) - [Hermes](https://composio.dev/toolkits/stack_ai/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/stack_ai/framework/cli) - [Google ADK](https://composio.dev/toolkits/stack_ai/framework/google-adk) - [LangChain](https://composio.dev/toolkits/stack_ai/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/stack_ai/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/stack_ai/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/stack_ai/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/stack_ai/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 Stack Ai - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Stack Ai tools - Run a live chat loop where you ask the agent to perform Stack Ai 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 Stack Ai MCP server, and what's possible with it? The Stack Ai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Stack Ai account. It provides structured and secure access so your agent can perform Stack Ai operations on your behalf. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `STACK_AI_CHECK_HEALTH` | Check Health | Tool to check the health status of the Stack AI API. Use to verify API availability and service status. | | `STACK_AI_GET_ACTION_INPUTS` | Get Action Inputs | Tool to retrieve the input schema for a specific provider action in Stack AI. Use when you need to understand what parameters are required for a provider action. | | `STACK_AI_GET_ACTION_OUTPUTS` | Get Action Output Schema | Tool to retrieve the output parameters schema for a Stack.ai provider action as JSON schema. Use when you need to understand what data fields an action returns or to validate action outputs. | | `STACK_AI_GET_CONNECTOR_TYPE_SCHEMA` | Get Connector Type Schema | Tool to retrieve the configuration schema for a specific connector type in Stack AI. Use when you need to understand what parameters are required to configure a connector. | | `STACK_AI_GET_LICENSE_STATUS` | Get License Status | Tool to retrieve the current Stack AI license status. Use when you need to check license validity, expiration date, or days remaining. | | `STACK_AI_GET_PROVIDER_DETAILS` | Get Provider Details | Tool to retrieve details of a specific Stack AI tool provider. Use when you need information about available actions, triggers, and configuration for a provider. | | `STACK_AI_GET_PROVIDER_ACTION_DETAILS` | Get Provider Action Details | Tool to get details of a specific action for a provider. Use when you need information about a provider's action including its parameters, description, and API details. | | `STACK_AI_GET_PROVIDER_ICON` | Get Provider Icon | Tool to fetch a provider icon image by provider identifier. Use when you need to retrieve the icon for a tool provider. | | `STACK_AI_GET_PROVIDER_TRIGGER_DETAILS` | Get Provider Trigger Details | Tool to retrieve detailed information about a specific trigger for a provider. Use when you need to understand the configuration, inputs, outputs, or behavior of a specific trigger. | | `STACK_AI_GET_ROOT` | Get Root | Tool to retrieve information from the Stack AI API root endpoint. Use when you need to verify API connectivity or get basic API information. | | `STACK_AI_GET_TRIGGER_DETAILS_FROM_PROVIDER` | Get Trigger Details From Provider | Tool to retrieve detailed information about a specific trigger from a provider. Use when you need to get trigger configuration, capabilities, or metadata for a specific provider's trigger. | | `STACK_AI_GET_TRIGGER_INPUTS` | Get Trigger Inputs | Tool to retrieve the input parameters for a trigger as a JSON schema. Use when discovering what data inputs a specific trigger requires before executing it. | | `STACK_AI_GET_TRIGGER_OUTPUTS` | Get Trigger Outputs | Tool to retrieve the output schema for a specific trigger in Stack AI. Use when you need to understand what fields a trigger will produce when it fires. This action helps discover the structure of data that will be available from a trigger event, which is useful for configuring workflows and data processing. | | `STACK_AI_LIST_CONNECTOR_TYPES` | List Connector Types | Tool to list all available connector types from Stack AI. Use when you need to retrieve the available connectors that can be configured. | | `STACK_AI_LIST_STACK_AI_INTEGRATIONS` | List Stack AI Integrations | Tool to list all available Stack AI integrations. Use when you need to discover available integrations, actions, and triggers in Stack AI. | | `STACK_AI_LIST_PERMISSION_GROUPS` | List Permission Groups | Tool to list all permission groups with their associated permissions. Use when you need to retrieve available permission groups and their permissions for access control management. | | `STACK_AI_LIST_PERMISSIONS` | List Permissions | Tool to list all available permissions in Stack AI. Use when you need to view or check available permission types. | | `STACK_AI_LIST_PROVIDER_TRIGGERS` | List Provider Triggers | Tool to get all available triggers for a specific provider. Use when you need to discover what trigger types are supported by a provider. | | `STACK_AI_LIST_STACK_AI_ACTIONS` | List Stack AI Actions | Tool to list all available Stack AI tool actions. Use when you need to discover available automation capabilities organized by provider. | | `STACK_AI_LIST_STACK_AI_PROVIDERS` | List Stack AI Providers | Tool to list all Stack AI tool providers (integrations). Use when you need to discover available integrations and their capabilities. Returns comprehensive information about each provider including available actions, triggers, and metadata. | | `STACK_AI_LIST_STACK_AI_BUILT_IN_TOOLS` | List Stack AI Built-in Tools | Tool to list all Stack AI built-in tools. Use when you need to discover available Stack AI native tools and their capabilities. | | `STACK_AI_LIST_STACK_AI_TRIGGERS` | List Stack AI Triggers | Tool to list all available Stack AI tool triggers. Use when you need to discover what triggers are available in the Stack AI platform. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Stack Ai MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Stack Ai. Instead of manually wiring Stack Ai 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 Stack Ai 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 Stack Ai 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 Stack Ai 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 Stack Ai 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 Stack Ai session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["stack_ai"] ) 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 Stack Ai 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 Stack Ai assistant agent with MCP tools agent = AssistantAgent( name="stack_ai_assistant", description="An AI assistant that helps with Stack Ai 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 Stack Ai 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 Stack Ai 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 Stack Ai session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["stack_ai"] ) 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 Stack Ai assistant agent with MCP tools agent = AssistantAgent( name="stack_ai_assistant", description="An AI assistant that helps with Stack Ai 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 Stack Ai 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 Stack Ai 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 Stack Ai, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Stack Ai MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/stack_ai/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/stack_ai/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/stack_ai/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/stack_ai/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/stack_ai/framework/codex) - [OpenClaw](https://composio.dev/toolkits/stack_ai/framework/openclaw) - [Hermes](https://composio.dev/toolkits/stack_ai/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/stack_ai/framework/cli) - [Google ADK](https://composio.dev/toolkits/stack_ai/framework/google-adk) - [LangChain](https://composio.dev/toolkits/stack_ai/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/stack_ai/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/stack_ai/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/stack_ai/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/stack_ai/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. - [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed. - [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. - [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service. - [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. - [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. - [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows. - [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow. - [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. - [Ai ml api](https://composio.dev/toolkits/ai_ml_api) - Ai ml api is a suite of AI/ML models for natural language and image tasks. It provides fast, scalable access to advanced AI capabilities for your apps and workflows. - [Aivoov](https://composio.dev/toolkits/aivoov) - Aivoov is an AI-powered text-to-speech platform offering 1,000+ voices in over 150 languages. Instantly turn written content into natural, human-like audio for any application. - [All images ai](https://composio.dev/toolkits/all_images_ai) - All-Images.ai is an AI-powered image generation and management platform. It helps you create, search, and organize images effortlessly with advanced AI capabilities. - [Anthropic administrator](https://composio.dev/toolkits/anthropic_administrator) - Anthropic administrator is an API for managing Anthropic organizational resources like members, workspaces, and API keys. It helps you automate admin tasks and streamline resource management across your Anthropic organization. - [Api labz](https://composio.dev/toolkits/api_labz) - Api labz is a platform offering a suite of AI-driven APIs and workflow tools. It helps developers automate tasks and build smarter, more efficient applications. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Stack Ai MCP? With a standalone Stack Ai MCP server, the agents and LLMs can only access a fixed set of Stack Ai tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Stack Ai 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 Stack Ai tools. ### Can I manage the permissions and scopes for Stack Ai while using Tool Router? Yes, absolutely. You can configure which Stack Ai 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 Stack Ai data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)