# How to integrate Timelinesai MCP with Pydantic AI ```json { "title": "How to integrate Timelinesai MCP with Pydantic AI", "toolkit": "Timelinesai", "toolkit_slug": "timelinesai", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/timelinesai/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/timelinesai/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:28:34.037Z" } ``` ## Introduction This guide walks you through connecting Timelinesai to Pydantic AI 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 Pydantic AI 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) - [CrewAI](https://composio.dev/toolkits/timelinesai/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up your Composio API key and User ID - How to create a Composio Tool Router session for Timelinesai - How to attach an MCP Server to a Pydantic AI agent - How to stream responses and maintain chat history - How to build a simple REPL-style chat interface to test your Timelinesai workflows ## What is Pydantic AI? Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications. Key features include: - Type Safety: Built on Pydantic for automatic data validation - MCP Support: Native support for Model Context Protocol servers - Streaming: Built-in support for streaming responses - Async First: Designed for async/await patterns ## 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 with an active API key - Basic familiarity with Python and async programming ### 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 the required libraries. What's happening: - composio connects your agent to external SaaS tools like Timelinesai - pydantic-ai lets you create structured AI agents with tool support - python-dotenv loads your environment variables securely from a .env file ```bash pip install composio pydantic-ai python-dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's happening: - COMPOSIO_API_KEY authenticates your agent to Composio's API - USER_ID associates your session with your account for secure tool access - OPENAI_API_KEY to access OpenAI LLMs ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key ``` ### 4. Import dependencies What's happening: - We load environment variables and import required modules - Composio manages connections to Timelinesai - MCPServerStreamableHTTP connects to the Timelinesai MCP server endpoint - Agent from Pydantic AI lets you define and run the AI assistant ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from pydantic_ai import Agent from pydantic_ai.mcp import MCPServerStreamableHTTP load_dotenv() ``` ### 5. Create a Tool Router Session What's happening: - We're creating a Tool Router session that gives your agent access to Timelinesai tools - The create method takes the user ID and specifies which toolkits should be available - The returned session.mcp.url is the MCP server URL that your agent will use ```python async def main(): api_key = os.getenv("COMPOSIO_API_KEY") user_id = os.getenv("USER_ID") if not api_key or not user_id: raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment") # Create a Composio Tool Router session for Timelinesai composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["timelinesai"], ) url = session.mcp.url if not url: raise ValueError("Composio session did not return an MCP URL") ``` ### 6. Initialize the Pydantic AI Agent What's happening: - The MCP client connects to the Timelinesai endpoint - The agent uses GPT-5 to interpret user commands and perform Timelinesai operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent timelinesai_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[timelinesai_mcp], instructions=( "You are a Timelinesai assistant. Use Timelinesai tools to help users " "with their requests. Ask clarifying questions when needed." ), ) ``` ### 7. Build the chat interface What's happening: - The agent reads input from the terminal and streams its response - Timelinesai API calls happen automatically under the hood - The model keeps conversation history to maintain context across turns ```python # Simple REPL with message history history = [] print("Chat started! Type 'exit' or 'quit' to end.\n") print("Try asking the agent to help you with Timelinesai.\n") 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", flush=True) async with agent.run_stream(user_input, message_history=history) as stream_result: collected_text = "" async for chunk in stream_result.stream_output(): text_piece = None if isinstance(chunk, str): text_piece = chunk elif hasattr(chunk, "delta") and isinstance(chunk.delta, str): text_piece = chunk.delta elif hasattr(chunk, "text"): text_piece = chunk.text if text_piece: collected_text += text_piece result = stream_result print(f"Agent: {collected_text}\n") history = result.all_messages() ``` ### 8. Run the application What's happening: - The asyncio loop launches the agent and keeps it running until you exit ```python if __name__ == "__main__": asyncio.run(main()) ``` ## Complete Code ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from pydantic_ai import Agent from pydantic_ai.mcp import MCPServerStreamableHTTP load_dotenv() async def main(): api_key = os.getenv("COMPOSIO_API_KEY") user_id = os.getenv("USER_ID") if not api_key or not user_id: raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment") # Create a Composio Tool Router session for Timelinesai composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["timelinesai"], ) url = session.mcp.url if not url: raise ValueError("Composio session did not return an MCP URL") # Attach the MCP server to a Pydantic AI Agent timelinesai_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[timelinesai_mcp], instructions=( "You are a Timelinesai assistant. Use Timelinesai tools to help users " "with their requests. Ask clarifying questions when needed." ), ) # Simple REPL with message history history = [] print("Chat started! Type 'exit' or 'quit' to end.\n") print("Try asking the agent to help you with Timelinesai.\n") 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", flush=True) async with agent.run_stream(user_input, message_history=history) as stream_result: collected_text = "" async for chunk in stream_result.stream_output(): text_piece = None if isinstance(chunk, str): text_piece = chunk elif hasattr(chunk, "delta") and isinstance(chunk.delta, str): text_piece = chunk.delta elif hasattr(chunk, "text"): text_piece = chunk.text if text_piece: collected_text += text_piece result = stream_result print(f"Agent: {collected_text}\n") history = result.all_messages() if __name__ == "__main__": asyncio.run(main()) ``` ## Conclusion You've built a Pydantic AI agent that can interact with Timelinesai through Composio's Tool Router. With this setup, your agent can perform real Timelinesai actions through natural language. You can extend this further by: - Adding other toolkits like Gmail, HubSpot, or Salesforce - Building a web-based chat interface around this agent - Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Timelinesai for workflow automation) This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations. ## 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) - [CrewAI](https://composio.dev/toolkits/timelinesai/framework/crew-ai) ## 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 Pydantic AI? Yes, you can. Pydantic AI 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)