# How to integrate Timelinesai MCP with Vercel AI SDK v6 ```json { "title": "How to integrate Timelinesai MCP with Vercel AI SDK v6", "toolkit": "Timelinesai", "toolkit_slug": "timelinesai", "framework": "Vercel AI SDK", "framework_slug": "ai-sdk", "url": "https://composio.dev/toolkits/timelinesai/framework/ai-sdk", "markdown_url": "https://composio.dev/toolkits/timelinesai/framework/ai-sdk.md", "updated_at": "2026-05-12T10:28:34.037Z" } ``` ## Introduction This guide walks you through connecting Timelinesai to Vercel AI SDK v6 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 Vercel AI SDK 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) - [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 and configure a Vercel AI SDK agent with Timelinesai integration - Using Composio's Tool Router to dynamically load and access Timelinesai tools - Creating an MCP client connection using HTTP transport - Building an interactive CLI chat interface with conversation history management - Handling tool calls and results within the Vercel AI SDK framework ## What is Vercel AI SDK? The Vercel AI SDK is a TypeScript library for building AI-powered applications. It provides tools for creating agents that can use external services and maintain conversation state. Key features include: - streamText: Core function for streaming responses with real-time tool support - MCP Client: Built-in support for Model Context Protocol via @ai-sdk/mcp - Step Counting: Control multi-step tool execution with stopWhen: stepCountIs() - OpenAI Provider: Native integration with OpenAI models ## 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 you begin, make sure you have: - Node.js and npm installed - A Composio account with API key - An OpenAI API key ### 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 required dependencies First, install the necessary packages for your project. What you're installing: - @ai-sdk/openai: Vercel AI SDK's OpenAI provider - @ai-sdk/mcp: MCP client for Vercel AI SDK - @composio/core: Composio SDK for tool integration - ai: Core Vercel AI SDK - dotenv: Environment variable management ```bash npm install @ai-sdk/openai @ai-sdk/mcp @composio/core ai dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's needed: - OPENAI_API_KEY: Your OpenAI API key for GPT model access - COMPOSIO_API_KEY: Your Composio API key for tool access - COMPOSIO_USER_ID: A unique identifier for the user session ```bash OPENAI_API_KEY=your_openai_api_key_here COMPOSIO_API_KEY=your_composio_api_key_here COMPOSIO_USER_ID=your_user_id_here ``` ### 4. Import required modules and validate environment What's happening: - We're importing all necessary libraries including Vercel AI SDK's OpenAI provider and Composio - The dotenv/config import automatically loads environment variables - The MCP client import enables connection to Composio's tool server ```typescript import "dotenv/config"; import { openai } from "@ai-sdk/openai"; import { Composio } from "@composio/core"; import * as readline from "readline"; import { streamText, type ModelMessage, stepCountIs } from "ai"; import { createMCPClient } from "@ai-sdk/mcp"; const composioAPIKey = process.env.COMPOSIO_API_KEY; const composioUserID = process.env.COMPOSIO_USER_ID; if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set"); if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set"); if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set"); const composio = new Composio({ apiKey: composioAPIKey, }); ``` ### 5. Create Tool Router session and initialize MCP client 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 mcp object contains the URL and authentication headers needed to connect to the MCP server - This session provides access to all Timelinesai-related tools through the MCP protocol ```typescript async function main() { // Create a tool router session for the user const session = await composio.create(composioUserID!, { toolkits: ["timelinesai"], }); const mcpUrl = session.mcp.url; ``` ### 6. Connect to MCP server and retrieve tools What's happening: - We're creating an MCP client that connects to our Composio Tool Router session via HTTP - The mcp.url provides the endpoint, and mcp.headers contains authentication credentials - The type: "http" is important - Composio requires HTTP transport - tools() retrieves all available Timelinesai tools that the agent can use ```typescript const mcpClient = await createMCPClient({ transport: { type: "http", url: mcpUrl, headers: session.mcp.headers, // Authentication headers for the Composio MCP server }, }); const tools = await mcpClient.tools(); ``` ### 7. Initialize conversation and CLI interface What's happening: - We initialize an empty messages array to maintain conversation history - A readline interface is created to accept user input from the command line - Instructions are displayed to guide the user on how to interact with the agent ```typescript let messages: ModelMessage[] = []; console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n"); console.log( "Ask any questions related to timelinesai, like summarize my last 5 emails, send an email, etc... :)))\n", ); const rl = readline.createInterface({ input: process.stdin, output: process.stdout, prompt: "> ", }); rl.prompt(); ``` ### 8. Handle user input and stream responses with real-time tool feedback What's happening: - We use streamText instead of generateText to stream responses in real-time - toolChoice: "auto" allows the model to decide when to use Timelinesai tools - stopWhen: stepCountIs(10) allows up to 10 steps for complex multi-tool operations - onStepFinish callback displays which tools are being used in real-time - We iterate through the text stream to create a typewriter effect as the agent responds - The complete response is added to conversation history to maintain context - Errors are caught and displayed with helpful retry suggestions ```typescript rl.on("line", async (userInput: string) => { const trimmedInput = userInput.trim(); if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) { console.log("\nGoodbye!"); rl.close(); process.exit(0); } if (!trimmedInput) { rl.prompt(); return; } messages.push({ role: "user", content: trimmedInput }); console.log("\nAgent is thinking...\n"); try { const stream = streamText({ model: openai("gpt-5"), messages, tools, toolChoice: "auto", stopWhen: stepCountIs(10), onStepFinish: (step) => { for (const toolCall of step.toolCalls) { console.log(`[Using tool: ${toolCall.toolName}]`); } if (step.toolCalls.length > 0) { console.log(""); // Add space after tool calls } }, }); for await (const chunk of stream.textStream) { process.stdout.write(chunk); } console.log("\n\n---\n"); // Get final result for message history const response = await stream.response; if (response?.messages?.length) { messages.push(...response.messages); } } catch (error) { console.error("\nAn error occurred while talking to the agent:"); console.error(error); console.log( "\nYou can try again or restart the app if it keeps happening.\n", ); } finally { rl.prompt(); } }); rl.on("close", async () => { await mcpClient.close(); console.log("\n👋 Session ended."); process.exit(0); }); } main().catch((err) => { console.error("Fatal error:", err); process.exit(1); }); ``` ## Complete Code ```typescript import "dotenv/config"; import { openai } from "@ai-sdk/openai"; import { Composio } from "@composio/core"; import * as readline from "readline"; import { streamText, type ModelMessage, stepCountIs } from "ai"; import { createMCPClient } from "@ai-sdk/mcp"; const composioAPIKey = process.env.COMPOSIO_API_KEY; const composioUserID = process.env.COMPOSIO_USER_ID; if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set"); if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set"); if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set"); const composio = new Composio({ apiKey: composioAPIKey, }); async function main() { // Create a tool router session for the user const session = await composio.create(composioUserID!, { toolkits: ["timelinesai"], }); const mcpUrl = session.mcp.url; const mcpClient = await createMCPClient({ transport: { type: "http", url: mcpUrl, headers: session.mcp.headers, // Authentication headers for the Composio MCP server }, }); const tools = await mcpClient.tools(); let messages: ModelMessage[] = []; console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n"); console.log( "Ask any questions related to timelinesai, like summarize my last 5 emails, send an email, etc... :)))\n", ); const rl = readline.createInterface({ input: process.stdin, output: process.stdout, prompt: "> ", }); rl.prompt(); rl.on("line", async (userInput: string) => { const trimmedInput = userInput.trim(); if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) { console.log("\nGoodbye!"); rl.close(); process.exit(0); } if (!trimmedInput) { rl.prompt(); return; } messages.push({ role: "user", content: trimmedInput }); console.log("\nAgent is thinking...\n"); try { const stream = streamText({ model: openai("gpt-5"), messages, tools, toolChoice: "auto", stopWhen: stepCountIs(10), onStepFinish: (step) => { for (const toolCall of step.toolCalls) { console.log(`[Using tool: ${toolCall.toolName}]`); } if (step.toolCalls.length > 0) { console.log(""); // Add space after tool calls } }, }); for await (const chunk of stream.textStream) { process.stdout.write(chunk); } console.log("\n\n---\n"); // Get final result for message history const response = await stream.response; if (response?.messages?.length) { messages.push(...response.messages); } } catch (error) { console.error("\nAn error occurred while talking to the agent:"); console.error(error); console.log( "\nYou can try again or restart the app if it keeps happening.\n", ); } finally { rl.prompt(); } }); rl.on("close", async () => { await mcpClient.close(); console.log("\n👋 Session ended."); process.exit(0); }); } main().catch((err) => { console.error("Fatal error:", err); process.exit(1); }); ``` ## Conclusion You've successfully built a Timelinesai agent using the Vercel AI SDK with streaming capabilities! This implementation provides a powerful foundation for building AI applications with natural language interfaces and real-time feedback. Key features of this implementation: - Real-time streaming responses for a better user experience with typewriter effect - Live tool execution feedback showing which tools are being used as the agent works - Dynamic tool loading through Composio's Tool Router with secure authentication - Multi-step tool execution with configurable step limits (up to 10 steps) - Comprehensive error handling for robust agent execution - Conversation history maintenance for context-aware responses You can extend this further by adding custom error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows. ## 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) - [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 Vercel AI SDK v6? Yes, you can. Vercel AI SDK v6 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)