# How to integrate Keen io MCP with CrewAI ```json { "title": "How to integrate Keen io MCP with CrewAI", "toolkit": "Keen io", "toolkit_slug": "keen_io", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/keen_io/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/keen_io/framework/crew-ai.md", "updated_at": "2026-05-12T10:16:44.227Z" } ``` ## Introduction This guide walks you through connecting Keen io to CrewAI 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 CrewAI 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) ## TL;DR Here's what you'll learn: - Get a Composio API key and configure your Keen io connection - Set up CrewAI with an MCP enabled agent - Create a Tool Router session or standalone MCP server for Keen io - Build a conversational loop where your agent can execute Keen io operations ## What is CrewAI? CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews. Key features include: - Agent Roles: Define specialized agents with specific goals and backstories - Task Management: Create tasks with clear descriptions and expected outputs - Crew Orchestration: Combine agents and tasks into collaborative workflows - MCP Integration: Connect to external tools through Model Context Protocol ## 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 agent to Keen io. It provides structured and secure access so your agent can perform Keen io 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 and API key - A Keen io connection authorized in Composio - An OpenAI API key for the CrewAI LLM - Basic familiarity with Python ### 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 **What's happening:** - composio connects your agent to Keen io via MCP - crewai provides Agent, Task, Crew, and LLM primitives - crewai-tools[mcp] includes MCP helpers - python-dotenv loads environment variables from .env ```bash pip install composio crewai crewai-tools[mcp] python-dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's happening: - COMPOSIO_API_KEY authenticates with Composio - USER_ID scopes the session to your account - OPENAI_API_KEY lets CrewAI use your chosen OpenAI model ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key_here ``` ### 4. Import dependencies **What's happening:** - CrewAI classes define agents and tasks, and run the workflow - MCPServerHTTP connects the agent to an MCP endpoint - Composio will give you a short lived Keen io MCP URL ```python import os from composio import Composio from crewai import Agent, Task, Crew from crewai_tools import MCPServerAdapter import dotenv dotenv.load_dotenv() COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set") ``` ### 5. Create a Composio Tool Router session for Keen io **What's happening:** - You create a Keen io only session through Composio - Composio returns an MCP HTTP URL that exposes Keen io tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["keen_io"]) url = session.mcp.url ``` ### 6. Initialize the MCP Server **What's Happening:** - Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication. - MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format. - Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools. - Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops. - Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries. ```python server_params = { "url": url, "transport": "streamable-http", "headers": {"x-api-key": COMPOSIO_API_KEY}, } with MCPServerAdapter(server_params) as tools: agent = Agent( role="Search Assistant", goal="Help users search the internet effectively", backstory="You are a helpful assistant with access to search tools.", tools=tools, verbose=False, max_iter=10, ) ``` ### 7. Create a CLI Chatloop and define the Crew **What's Happening:** - Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable. - Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean. - Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses. - Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context. - Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response. - Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity. ```python print("Chat started! Type 'exit' or 'quit' to end.\n") conversation_context = "" while True: user_input = input("You: ").strip() if user_input.lower() in ["exit", "quit", "bye"]: print("\nGoodbye!") break if not user_input: continue conversation_context += f"\nUser: {user_input}\n" print("\nAgent is thinking...\n") task = Task( description=( f"Conversation history:\n{conversation_context}\n\n" f"Current request: {user_input}" ), expected_output="A helpful response addressing the user's request", agent=agent, ) crew = Crew(agents=[agent], tasks=[task], verbose=False) result = crew.kickoff() response = str(result) conversation_context += f"Agent: {response}\n" print(f"Agent: {response}\n") ``` ## Complete Code ```python from crewai import Agent, Task, Crew, LLM from crewai_tools import MCPServerAdapter from composio import Composio from dotenv import load_dotenv import os load_dotenv() GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY") COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not GOOGLE_API_KEY: raise ValueError("GOOGLE_API_KEY is not set in the environment.") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set in the environment.") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set in the environment.") # Initialize Composio and create a session composio = Composio(api_key=COMPOSIO_API_KEY) session = composio.create( user_id=COMPOSIO_USER_ID, toolkits=["keen_io"], ) url = session.mcp.url # Configure LLM llm = LLM( model="gpt-5", api_key=os.getenv("OPENAI_API_KEY"), ) server_params = { "url": url, "transport": "streamable-http", "headers": {"x-api-key": COMPOSIO_API_KEY}, } with MCPServerAdapter(server_params) as tools: agent = Agent( role="Search Assistant", goal="Help users with internet searches", backstory="You are an expert assistant with access to Composio Search tools.", tools=tools, llm=llm, verbose=False, max_iter=10, ) print("Chat started! Type 'exit' or 'quit' to end.\n") conversation_context = "" while True: user_input = input("You: ").strip() if user_input.lower() in ["exit", "quit", "bye"]: print("\nGoodbye!") break if not user_input: continue conversation_context += f"\nUser: {user_input}\n" print("\nAgent is thinking...\n") task = Task( description=( f"Conversation history:\n{conversation_context}\n\n" f"Current request: {user_input}" ), expected_output="A helpful response addressing the user's request", agent=agent, ) crew = Crew(agents=[agent], tasks=[task], verbose=False) result = crew.kickoff() response = str(result) conversation_context += f"Agent: {response}\n" print(f"Agent: {response}\n") ``` ## Conclusion You now have a CrewAI agent connected to Keen io through Composio's Tool Router. The agent can perform Keen io operations through natural language commands. Next steps: - Add role-specific instructions to customize agent behavior - Plug in more toolkits for multi-app workflows - Chain tasks for complex multi-step operations ## 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) ## Related Toolkits - [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. - [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. - [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. - [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. - [Bright Data MCP](https://composio.dev/toolkits/brightdata_mcp) - Bright Data MCP is an AI-powered web scraping and data collection platform. Instantly access public web data in real time with advanced scraping tools. - [Browseai](https://composio.dev/toolkits/browseai) - Browseai is a web automation and data extraction platform that turns any website into an API. It's perfect for monitoring websites and retrieving structured data without manual scraping. - [ClickHouse](https://composio.dev/toolkits/clickhouse) - ClickHouse is an open-source, column-oriented database for real-time analytics and big data processing using SQL. Its lightning-fast query performance makes it ideal for handling large datasets and delivering instant insights. - [Coinmarketcal](https://composio.dev/toolkits/coinmarketcal) - CoinMarketCal is a community-powered crypto calendar for upcoming events, announcements, and releases. It helps traders track market-moving developments and stay ahead in the crypto space. - [Control d](https://composio.dev/toolkits/control_d) - Control d is a customizable DNS filtering and traffic redirection platform. It helps you manage internet access, enforce policies, and monitor usage across devices and networks. - [Databox](https://composio.dev/toolkits/databox) - Databox is a business analytics platform that connects your data from any tool and device. It helps you track KPIs, build dashboards, and discover actionable insights. - [Databricks](https://composio.dev/toolkits/databricks) - Databricks is a unified analytics platform for big data and AI on the lakehouse architecture. It empowers data teams to collaborate, analyze, and build scalable solutions efficiently. - [Datagma](https://composio.dev/toolkits/datagma) - Datagma delivers data intelligence and analytics for business growth and market discovery. Get actionable market insights and track competitors to inform your strategy. - [Delighted](https://composio.dev/toolkits/delighted) - Delighted is a customer feedback platform based on the Net Promoter System®. 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 CrewAI? Yes, you can. CrewAI 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)