# How to integrate Keen io MCP with Autogen ```json { "title": "How to integrate Keen io MCP with Autogen", "toolkit": "Keen io", "toolkit_slug": "keen_io", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/keen_io/framework/autogen", "markdown_url": "https://composio.dev/toolkits/keen_io/framework/autogen.md", "updated_at": "2026-05-12T10:16:44.227Z" } ``` ## Introduction This guide walks you through connecting Keen io to AutoGen using the Composio tool router. By the end, you'll have a working Keen io agent that can list all event collections in your project, show unique user ids from purchases collection, inspect schema for app_signups collection through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Keen io account through Composio's Keen io MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Keen io with - [OpenAI Agents SDK](https://composio.dev/toolkits/keen_io/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/keen_io/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/keen_io/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/keen_io/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/keen_io/framework/codex) - [OpenClaw](https://composio.dev/toolkits/keen_io/framework/openclaw) - [Hermes](https://composio.dev/toolkits/keen_io/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/keen_io/framework/cli) - [Google ADK](https://composio.dev/toolkits/keen_io/framework/google-adk) - [LangChain](https://composio.dev/toolkits/keen_io/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/keen_io/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/keen_io/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/keen_io/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/keen_io/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 Keen io - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Keen io tools - Run a live chat loop where you ask the agent to perform Keen io 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 Keen io MCP server, and what's possible with it? The Keen io MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Keen io account. It provides structured and secure access to your event data and analytics projects, so your agent can inspect event collections, analyze properties, fetch unique values, manage cached datasets, and even help with access key administration—all on your behalf. - Comprehensive event collection inspection: Let your agent list all event collections in your project and retrieve detailed schema information for each, so you always know what data is available. - Property analysis and schema insights: Have the agent dive into specific properties within a collection to reveal inferred types and resource URLs for precise data understanding. - Unique value extraction: Direct your agent to fetch all unique values for any property across your events, making it easy to spot trends, segments, or outliers in your analytics. - Cached dataset management: Ask your agent to list and page through all cached dataset definitions in your Keen io project for streamlined reporting and analysis workflows. - Access key administration: Instruct your agent to unrevoke previously revoked API keys, helping you quickly restore secure access when needed. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `KEEN_IO_INSPECT_ALL_EVENT_COLLECTIONS` | Inspect All Event Collections | Retrieve schema information for all event collections in a Keen.io project. Use this tool to: - List all event collection names in a project - Discover the schema (property names and types) for each collection - Explore available data before running queries Returns up to 5000 event collections with their names, URLs, and optional property schemas. | | `KEEN_IO_INSPECT_COLLECTION_PROPERTY` | Inspect Collection Property | Tool to return details for a specific property in an event collection. Use when you need to inspect a property's inferred type and resource URL. | | `KEEN_IO_INSPECT_SINGLE_EVENT_COLLECTION` | Inspect Single Event Collection | Retrieve schema information for a single Keen.io event collection. Returns the inferred property types for all fields in the specified collection, useful for understanding data structure before running queries. Property types include 'num' (numbers), 'string' (text), 'bool' (booleans), and 'datetime' (timestamps). Use this tool when you need to: - Understand the structure of a specific event collection - Verify property names and types before building queries - Debug data type mismatches in analytics queries | | `KEEN_IO_LIST_CACHED_DATASETS` | List Cached Dataset Definitions | List all cached dataset definitions for a Keen.io project. Returns paginated results of pre-computed dataset definitions including their query configuration, status, and timing information. Use limit and after_name parameters to page through large result sets. Cached datasets allow pre-computing analytics for hundreds or thousands of entities at once, enabling instant retrieval of results for any indexed entity. | | `KEEN_IO_SELECT_UNIQUE` | Select Unique | Tool to return unique values for a target property. Use when distinct property values are required for matching events with optional filters and timeframe constraints. | | `KEEN_IO_UNREVOKE_ACCESS_KEY` | Unrevoke Access Key | Reactivate a previously revoked Keen.io access key. Use this tool when you need to restore access for a key that was previously revoked but not deleted. A revoked key has its 'active' flag set to false; this operation sets it back to true, allowing the key to be used for API authentication again. Note: This operation requires a Master API Key for authentication. The key must have been previously revoked (not deleted) to be unrevoked. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Keen io MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Keen io. Instead of manually wiring Keen io 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 Keen io 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 Keen io 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 Keen io 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 Keen io 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 Keen io session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["keen_io"] ) 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 Keen io 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 Keen io assistant agent with MCP tools agent = AssistantAgent( name="keen_io_assistant", description="An AI assistant that helps with Keen io 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 Keen io 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 Keen io 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 Keen io session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["keen_io"] ) 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 Keen io assistant agent with MCP tools agent = AssistantAgent( name="keen_io_assistant", description="An AI assistant that helps with Keen io 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 Keen io 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 Keen io 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 Keen io, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Keen io MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/keen_io/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/keen_io/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/keen_io/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/keen_io/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/keen_io/framework/codex) - [OpenClaw](https://composio.dev/toolkits/keen_io/framework/openclaw) - [Hermes](https://composio.dev/toolkits/keen_io/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/keen_io/framework/cli) - [Google ADK](https://composio.dev/toolkits/keen_io/framework/google-adk) - [LangChain](https://composio.dev/toolkits/keen_io/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/keen_io/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/keen_io/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/keen_io/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/keen_io/framework/crew-ai) ## Related Toolkits - [Firecrawl](https://composio.dev/toolkits/firecrawl) - Firecrawl automates large-scale web crawling and data extraction. 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It helps you quickly gather, track, and act on customer sentiment. - [Dovetail](https://composio.dev/toolkits/dovetail) - Dovetail is a research analysis platform for transcript review and insight generation. It helps teams code interviews, analyze feedback, and create actionable research summaries. - [Dub](https://composio.dev/toolkits/dub) - Dub is a short link management platform with analytics and API access. Use it to easily create, manage, and track branded short links for your business. - [Elasticsearch](https://composio.dev/toolkits/elasticsearch) - Elasticsearch is a distributed, RESTful search and analytics engine for all types of data. It delivers fast, scalable search and powerful analytics across massive datasets. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Keen io MCP? With a standalone Keen io MCP server, the agents and LLMs can only access a fixed set of Keen io tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Keen io 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 Keen io tools. ### Can I manage the permissions and scopes for Keen io while using Tool Router? Yes, absolutely. You can configure which Keen io 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 Keen io data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)