# How to integrate Timelinesai MCP with OpenAI Agents SDK

```json
{
  "title": "How to integrate Timelinesai MCP with OpenAI Agents SDK",
  "toolkit": "Timelinesai",
  "toolkit_slug": "timelinesai",
  "framework": "OpenAI Agents SDK",
  "framework_slug": "open-ai-agents-sdk",
  "url": "https://composio.dev/toolkits/timelinesai/framework/open-ai-agents-sdk",
  "markdown_url": "https://composio.dev/toolkits/timelinesai/framework/open-ai-agents-sdk.md",
  "updated_at": "2026-05-12T10:28:34.037Z"
}
```

## Introduction

This guide walks you through connecting Timelinesai to the OpenAI Agents SDK 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 OpenAI Agents 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

- [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:
- Get and set up your OpenAI and Composio API keys
- Install the necessary dependencies
- Initialize Composio and create a Tool Router session for Timelinesai
- Configure an AI agent that can use Timelinesai as a tool
- Run a live chat session where you can ask the agent to perform Timelinesai operations

## What is OpenAI Agents SDK?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.
Key features include:
- Hosted MCP Tools: Connect to external services through hosted MCP endpoints
- SQLite Sessions: Persist conversation history across interactions
- Simple API: Clean interface with Agent, Runner, and tool configuration
- Streaming Support: Real-time response streaming for interactive applications

## 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:
- Composio API Key and OpenAI API Key
- Primary know-how of OpenAI Agents SDK
- A live Timelinesai project
- Some knowledge of Python or Typescript

### 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).
- Go to Settings and copy your API key.

### 2. Install dependencies

Install the Composio SDK and the OpenAI Agents SDK.
```python
pip install composio_openai_agents openai-agents python-dotenv
```

```typescript
npm install @composio/openai-agents @openai/agents dotenv
```

### 3. Set up environment variables

Create a .env file and add your OpenAI and Composio API keys.
```bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com
```

### 4. Import dependencies

What's happening:
- You're importing all necessary libraries.
- The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like Timelinesai.
```python
import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
```

```typescript
import 'dotenv/config';
import { Composio } from '@composio/core';
import { OpenAIAgentsProvider } from '@composio/openai-agents';
import { Agent, hostedMcpTool, run, OpenAIConversationsSession } from '@openai/agents';
import * as readline from 'readline';
```

### 5. Set up the Composio instance

No description provided.
```python
load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
```

```typescript
dotenv.config();

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.USER_ID;

if (!composioApiKey) {
  throw new Error('COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key');
}
if (!userId) {
  throw new Error('USER_ID is not set');
}

// Initialize Composio
const composio = new Composio({
  apiKey: composioApiKey,
  provider: new OpenAIAgentsProvider(),
});
```

### 6. Create a Tool Router session

What is happening:
- You give the Tool Router the user id and the toolkits you want available. Here, it is only timelinesai.
- The router checks the user's Timelinesai connection and prepares the MCP endpoint.
- The returned session.mcp.url is the MCP URL that your agent will use to access Timelinesai.
- This approach keeps things lightweight and lets the agent request Timelinesai tools only when needed during the conversation.
```python
# Create a Timelinesai Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["timelinesai"]
)

mcp_url = session.mcp.url
```

```typescript
// Create Tool Router session for Timelinesai
const session = await composio.create(userId as string, {
  toolkits: ['timelinesai'],
});
const mcpUrl = session.mcp.url;
```

### 7. Configure the agent

No description provided.
```python
# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Timelinesai. "
        "Help users perform Timelinesai operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
```

```typescript
// Configure agent with MCP tool
const agent = new Agent({
  name: 'Assistant',
  model: 'gpt-5',
  instructions:
    'You are a helpful assistant that can access Timelinesai. Help users perform Timelinesai operations through natural language.',
  tools: [
    hostedMcpTool({
      serverLabel: 'tool_router',
      serverUrl: mcpUrl,
      headers: { 'x-api-key': composioApiKey },
      requireApproval: 'never',
    }),
  ],
});
```

### 8. Start chat loop and handle conversation

No description provided.
```python
print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
```

```typescript
// Keep conversation state across turns
const conversationSession = new OpenAIConversationsSession();

// Simple CLI
const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: 'You: ',
});

console.log('\nComposio Tool Router session created.');
console.log('\nChat started. Type your requests below.');
console.log("Commands: 'exit', 'quit', or 'q' to end\n");

try {
  const first = await run(agent, 'What can you help me with?', { session: conversationSession });
  console.log(`Assistant: ${first.finalOutput}\n`);
} catch (e) {
  console.error('Error:', e instanceof Error ? e.message : e, '\n');
}

rl.prompt();

rl.on('line', async (userInput) => {
  const text = userInput.trim();

  if (['exit', 'quit', 'q'].includes(text.toLowerCase())) {
    console.log('Goodbye!');
    rl.close();
    process.exit(0);
  }

  if (!text) {
    rl.prompt();
    return;
  }

  try {
    const result = await run(agent, text, { session: conversationSession });
    console.log(`\nAssistant: ${result.finalOutput}\n`);
  } catch (e) {
    console.error('Error:', e instanceof Error ? e.message : e, '\n');
  }

  rl.prompt();
});

rl.on('close', () => {
  console.log('\n👋 Session ended.');
  process.exit(0);
});
```

## Complete Code

```python
import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["timelinesai"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Timelinesai. "
        "Help users perform Timelinesai operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
```

```typescript
import 'dotenv/config';
import { Composio } from '@composio/core';
import { OpenAIAgentsProvider } from '@composio/openai-agents';
import { Agent, hostedMcpTool, run, OpenAIConversationsSession } from '@openai/agents';
import * as readline from 'readline';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.USER_ID;

if (!composioApiKey) {
  throw new Error('COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key');
}
if (!userId) {
  throw new Error('USER_ID is not set');
}

// Initialize Composio
const composio = new Composio({
  apiKey: composioApiKey,
  provider: new OpenAIAgentsProvider(),
});

async function main() {
  // Create Tool Router session
  const session = await composio.create(userId as string, {
    toolkits: ['timelinesai'],
  });
  const mcpUrl = session.mcp.url;

  // Configure agent with MCP tool
  const agent = new Agent({
    name: 'Assistant',
    model: 'gpt-5',
    instructions:
      'You are a helpful assistant that can access Timelinesai. Help users perform Timelinesai operations through natural language.',
    tools: [
      hostedMcpTool({
        serverLabel: 'tool_router',
        serverUrl: mcpUrl,
        headers: { 'x-api-key': composioApiKey },
        requireApproval: 'never',
      }),
    ],
  });

  // Keep conversation state across turns
  const conversationSession = new OpenAIConversationsSession();

  // Simple CLI
  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: ',
  });

  console.log('\nComposio Tool Router session created.');
  console.log('\nChat started. Type your requests below.');
  console.log("Commands: 'exit', 'quit', or 'q' to end\n");

  try {
    const first = await run(agent, 'What can you help me with?', { session: conversationSession });
    console.log(`Assistant: ${first.finalOutput}\n`);
  } catch (e) {
    console.error('Error:', e instanceof Error ? e.message : e, '\n');
  }

  rl.prompt();

  rl.on('line', async (userInput) => {
    const text = userInput.trim();

    if (['exit', 'quit', 'q'].includes(text.toLowerCase())) {
      console.log('Goodbye!');
      rl.close();
      process.exit(0);
    }

    if (!text) {
      rl.prompt();
      return;
    }

    try {
      const result = await run(agent, text, { session: conversationSession });
      console.log(`\nAssistant: ${result.finalOutput}\n`);
    } catch (e) {
      console.error('Error:', e instanceof Error ? e.message : e, '\n');
    }

    rl.prompt();
  });

  rl.on('close', () => {
    console.log('\nSession ended.');
    process.exit(0);
  });
}

main().catch((err) => {
  console.error('Fatal error:', err);
  process.exit(1);
});
```

## Conclusion

This was a starter code for integrating Timelinesai MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Timelinesai.
Key features:
- Hosted MCP tool integration through Composio's Tool Router
- SQLite session persistence for conversation history
- Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

## How to build Timelinesai MCP Agent with another framework

- [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 OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK 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)