# How to integrate Timelinesai MCP with CrewAI ```json { "title": "How to integrate Timelinesai MCP with CrewAI", "toolkit": "Timelinesai", "toolkit_slug": "timelinesai", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/timelinesai/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/timelinesai/framework/crew-ai.md", "updated_at": "2026-05-12T10:28:34.037Z" } ``` ## Introduction This guide walks you through connecting Timelinesai to CrewAI using the Composio tool router. By the end, you'll have a working Timelinesai agent that can get the last 10 messages from sales chat, list all unread whatsapp chats assigned to me, create webhook for new incoming messages through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Timelinesai account through Composio's Timelinesai MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Timelinesai with - [OpenAI Agents SDK](https://composio.dev/toolkits/timelinesai/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/timelinesai/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/timelinesai/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/timelinesai/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/timelinesai/framework/codex) - [OpenClaw](https://composio.dev/toolkits/timelinesai/framework/openclaw) - [Hermes](https://composio.dev/toolkits/timelinesai/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/timelinesai/framework/cli) - [Google ADK](https://composio.dev/toolkits/timelinesai/framework/google-adk) - [LangChain](https://composio.dev/toolkits/timelinesai/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/timelinesai/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/timelinesai/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/timelinesai/framework/llama-index) ## TL;DR Here's what you'll learn: - Get a Composio API key and configure your Timelinesai connection - Set up CrewAI with an MCP enabled agent - Create a Tool Router session or standalone MCP server for Timelinesai - Build a conversational loop where your agent can execute Timelinesai 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 Timelinesai MCP server, and what's possible with it? The Timelinesai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Timelinesai account. It provides structured and secure access to your WhatsApp communications, so your agent can retrieve chat messages, manage files, automate webhook workflows, and keep your team’s communication organized—all on your behalf. - WhatsApp chat management: Fetch recent or historical messages from specific chats, or list all active and unread chats to help you stay on top of conversations. - Automated webhook integration: Set up, review, or delete webhook subscriptions to automate notifications and keep your workflows synced across tools. - File and attachment handling: List uploaded files, retrieve file details or secure download links, and delete files when they’re no longer needed. - WhatsApp account verification: Quickly list and verify all WhatsApp accounts connected to your workspace for streamlined onboarding and troubleshooting. - Workspace insight and cleanup: Get a comprehensive view of all webhooks or uploaded files, making workspace management and housekeeping a breeze. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `TIMELINESAI_DELETE_FILE` | Delete File | Tool to delete an uploaded file by its UID. Use after confirming the file is no longer needed. | | `TIMELINESAI_DELETE_WEBHOOK` | Delete Webhook | Tool to delete a webhook subscription by its ID. Use when you need to remove an existing webhook after confirming the webhook ID. Example: "Delete the webhook with ID '9f6a8c3d-56b7-4a1e-8f2e-abcdef123456'." | | `TIMELINESAI_GET_CHAT_MESSAGES` | Get Chat Messages | Tool to get messages from a specific chat in TimelinesAI. Use when you need to retrieve message history or recent messages from a chat. Example: "Get the last 20 messages from chat 'chat_123abc'." | | `TIMELINESAI_GET_CHATS` | Get Chats | Tool to get full or filtered list of all chats. Use when you need to browse or search chats with optional filters. Example: "List all unread chats assigned to me." | | `TIMELINESAI_GET_FILE_DETAILS` | Get File Details | Tool to retrieve metadata and temporary download URL for an uploaded file. Use after uploading a file or when needing its URL. | | `TIMELINESAI_GET_WEBHOOK` | Get Webhook | Retrieves detailed information about a specific webhook subscription by its ID. Use this action to: - Check webhook configuration (URL, event type, enabled status) - Monitor webhook health (error counter) - Verify webhook existence before updating or deleting Prerequisites: You must have a valid webhook ID. Use the Get Webhooks action to list all available webhooks first. | | `TIMELINESAI_GET_WEBHOOKS` | Get Webhooks | Retrieves all webhook subscriptions configured for the workspace. Webhooks notify external systems about events (e.g., 'message:new', 'chat:new') in real-time. Use this to view existing webhook configurations, check their status, or retrieve webhook IDs for updates/deletion. Supports optional pagination via limit and offset parameters. | | `TIMELINESAI_GET_WHATSAPP_ACCOUNTS` | Get WhatsApp Accounts | Tool to list all WhatsApp accounts connected to the workspace. Use after configuring WhatsApp integration to verify accounts. | | `TIMELINESAI_LIST_UPLOADED_FILES` | List Uploaded Files | Tool to list files uploaded in your TimelinesAI workspace. Use when you need to retrieve all uploaded files. | | `TIMELINESAI_POST_MESSAGE` | Send WhatsApp Message to Number | Tool to send a WhatsApp text message to a phone number via TimelinesAI. Use this to send messages to any recipient phone number using one of your connected WhatsApp accounts as the sender. The message will be delivered immediately if the recipient number is reachable on WhatsApp. Example: Send 'Hello, how can I help you today?' from +15105566777 to +14151231234. | | `TIMELINESAI_POST_WEBHOOK` | Create Webhook Subscription | Tool to create a new webhook subscription. Use when you have the event type and callback URL ready. | | `TIMELINESAI_PUT_WEBHOOK` | Update Webhook | Tool to update an existing webhook subscription. Use after confirming the webhook ID when you need to change the URL, subscribed event types, or enable/disable a webhook. | | `TIMELINESAI_SEND_MESSAGE` | Send Message to Chat | Send a WhatsApp message to an existing chat in TimelinesAI. Use this action when you have a chat ID from the Get Chats action and want to send a message to that conversation. For sending messages to new phone numbers (not existing chats), use the 'Send WhatsApp Message to Number' action instead. Example: "Send message 'Hello world' to chat with ID 'chat_123abc'." | ## Supported Triggers | Trigger slug | Name | Description | |---|---|---| | `TIMELINESAI_NEW_MESSAGE_RECEIVED` | New WhatsApp Message Received | Polling trigger that monitors for new messages received in TimelinesAI WhatsApp chats. | ## Creating MCP Server - Stand-alone vs Composio SDK The Timelinesai MCP server is an implementation of the Model Context Protocol that connects your AI agent to Timelinesai. It provides structured and secure access so your agent can perform Timelinesai 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 Timelinesai 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 Timelinesai 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 Timelinesai 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 Timelinesai **What's happening:** - You create a Timelinesai only session through Composio - Composio returns an MCP HTTP URL that exposes Timelinesai tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["timelinesai"]) 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=["timelinesai"], ) 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 Timelinesai through Composio's Tool Router. The agent can perform Timelinesai 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 Timelinesai MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/timelinesai/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/timelinesai/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/timelinesai/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/timelinesai/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/timelinesai/framework/codex) - [OpenClaw](https://composio.dev/toolkits/timelinesai/framework/openclaw) - [Hermes](https://composio.dev/toolkits/timelinesai/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/timelinesai/framework/cli) - [Google ADK](https://composio.dev/toolkits/timelinesai/framework/google-adk) - [LangChain](https://composio.dev/toolkits/timelinesai/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/timelinesai/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/timelinesai/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/timelinesai/framework/llama-index) ## Related Toolkits - [Gmail](https://composio.dev/toolkits/gmail) - Gmail is Google's email service with powerful spam protection, search, and G Suite integration. It keeps your inbox organized and makes communication fast and reliable. - [Outlook](https://composio.dev/toolkits/outlook) - Outlook is Microsoft's email and calendaring platform for unified communications and scheduling. It helps users stay organized with powerful email, contacts, and calendar management. - [Slack](https://composio.dev/toolkits/slack) - Slack is a channel-based messaging platform for teams and organizations. It helps people collaborate in real time, share files, and connect all their tools in one place. - [Gong](https://composio.dev/toolkits/gong) - Gong is a platform for video meetings, call recording, and team collaboration. It helps teams capture conversations, analyze calls, and turn insights into action. - [Microsoft teams](https://composio.dev/toolkits/microsoft_teams) - Microsoft Teams is a collaboration platform that combines chat, meetings, and file sharing within Microsoft 365. It keeps distributed teams connected and productive through seamless virtual communication. - [Slackbot](https://composio.dev/toolkits/slackbot) - Slackbot is a conversational automation tool for Slack that handles reminders, notifications, and automated responses. It boosts team productivity by streamlining onboarding, answering FAQs, and managing timely alerts—all right inside Slack. - [2chat](https://composio.dev/toolkits/_2chat) - 2chat is an API platform for WhatsApp and multichannel text messaging. It streamlines chat automation, group management, and real-time messaging for developers. - [Agent mail](https://composio.dev/toolkits/agent_mail) - Agent mail provides AI agents with dedicated email inboxes for sending, receiving, and managing emails. It empowers agents to communicate autonomously with people, services, and other agents—no human intervention needed. - [Basecamp](https://composio.dev/toolkits/basecamp) - Basecamp is a project management and team collaboration tool by 37signals. It helps teams organize tasks, share files, and communicate efficiently in one place. - [Chatwork](https://composio.dev/toolkits/chatwork) - Chatwork is a team communication platform with group chats, file sharing, and task management. It helps businesses boost collaboration and streamline productivity. - [Clickmeeting](https://composio.dev/toolkits/clickmeeting) - ClickMeeting is a cloud-based platform for running online meetings and webinars. It helps businesses and individuals host, manage, and engage virtual audiences with ease. - [Confluence](https://composio.dev/toolkits/confluence) - Confluence is Atlassian's team collaboration and knowledge management platform. It helps your team organize, share, and update documents and project content in one secure workspace. - [Dailybot](https://composio.dev/toolkits/dailybot) - DailyBot streamlines team collaboration with chat-based standups, reminders, and polls. It keeps work flowing smoothly in your favorite messaging platforms. - [Dialmycalls](https://composio.dev/toolkits/dialmycalls) - Dialmycalls is a mass notification service for sending voice and text messages to contacts. It helps teams and organizations quickly broadcast urgent alerts and updates. - [Dialpad](https://composio.dev/toolkits/dialpad) - Dialpad is a cloud-based business phone and contact center system for teams. It unifies voice, video, messaging, and meetings across your devices. - [Discord](https://composio.dev/toolkits/discord) - Discord is a real-time messaging and VoIP platform for communities and teams. It lets users chat, share media, and collaborate across public and private channels. - [Discordbot](https://composio.dev/toolkits/discordbot) - Discordbot is an automation tool for Discord servers that handles moderation, messaging, and user engagement. It helps communities run smoothly by automating routine and complex tasks. - [Echtpost](https://composio.dev/toolkits/echtpost) - Echtpost is a secure digital communication platform for encrypted document and message exchange. It ensures confidential data stays private and protected during transmission. - [Egnyte](https://composio.dev/toolkits/egnyte) - Egnyte is a cloud-based platform for secure file sharing, storage, and governance. It helps teams collaborate efficiently while maintaining data compliance and security. - [Google Meet](https://composio.dev/toolkits/googlemeet) - Google Meet is a secure video conferencing platform for virtual meetings, chat, and screen sharing. It helps teams connect, collaborate, and communicate seamlessly from anywhere. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Timelinesai MCP? With a standalone Timelinesai MCP server, the agents and LLMs can only access a fixed set of Timelinesai tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Timelinesai 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 Timelinesai tools. ### Can I manage the permissions and scopes for Timelinesai while using Tool Router? Yes, absolutely. You can configure which Timelinesai 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 Timelinesai data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)