How to integrate Toggl MCP with Autogen

This guide walks you through connecting Toggl to AutoGen using the Composio tool router. By the end, you'll have a working Toggl agent that can start a new time entry for coding, list all clients in your workspace, get details of your current running timer through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Toggl account through Composio's Toggl MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Toggl is a time tracking platform for managing work hours and productivity. It helps individuals and teams monitor tasks, analyze time usage, and optimize workflows.

56 Tools

Introduction

This guide walks you through connecting Toggl to AutoGen using the Composio tool router. By the end, you'll have a working Toggl agent that can start a new time entry for coding, list all clients in your workspace, get details of your current running timer through natural language commands.

This guide will help you understand how to give your AutoGen agent real control over a Toggl account through Composio's Toggl MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Toggl with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Toggl
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Toggl tools
  • Run a live chat loop where you ask the agent to perform Toggl operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

What is the Toggl MCP server, and what's possible with it?

The Toggl MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Toggl account. It provides structured and secure access to your time tracking data, so your agent can perform actions like logging time entries, managing clients and projects, handling tags, and retrieving detailed activity reports on your behalf.

  • Automated time entry management: Let your agent start, stop, and create new time entries with precise details, making it easy to track your work hours hands-free.
  • Client and project organization: Easily add new clients or projects, fetch client details, or remove outdated clients to keep your workspace up to date and well-structured.
  • Real-time activity tracking: Ask your agent to retrieve the currently running time entry or list recent activities, so you always know where your time is going.
  • Tag management and organization: Automatically create or delete tags to categorize your time entries, helping you analyze how your time is spent across different tasks.
  • Comprehensive workspace administration: Have your agent create organizations, set up workspaces, and ensure all your time tracking infrastructure is ready to go without manual setup.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Toggl account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard 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.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Toggl via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Toggl connections to use
5

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Toggl session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["toggl"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Toggl tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed
7

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Toggl assistant agent with MCP tools
    agent = AssistantAgent(
        name="toggl_assistant",
        description="An AI assistant that helps with Toggl operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Toggl tools from the workbench
8

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Toggl related question or task to the agent.\n")

# Conversation loop
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")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Toggl tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Toggl and AutoGen:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Toggl session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["toggl"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Toggl assistant agent with MCP tools
        agent = AssistantAgent(
            name="toggl_assistant",
            description="An AI assistant that helps with Toggl operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any Toggl related question or task to the agent.\n")

        # Conversation loop
        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")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You now have an Autogen assistant wired into Toggl through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Toggl, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
TOOLS

Supported Tools

Every Toggl action and event your agent gets out of the box.

Create Client

Tool to create a new client in a workspace.

Create Group

Tool to create a new group in a Toggl organization.

Create Invitation

Tool to send invitations to join a Toggl organization.

Create Organization

Tool to create a new organization with a default workspace in Toggl Track.

Create Project

Creates a new project in a Toggl workspace.

Create Tag

Tool to create a new tag in a workspace.

Create Time Entry

Tool to create a new time entry in the specified workspace.

Add User to Workspace Project

Tool to add a user to workspace project users.

Delete Toggl Client

Tool to delete a client in Toggl.

Delete Group

Tool to delete a group from a Toggl organization.

Delete Project Group

Tool to delete a project group from a Toggl workspace.

Delete Subscription

Tool to delete a webhook subscription in Toggl.

Delete Tag

Deletes a tag from a Toggl workspace.

Disable Weekly Report

Tool to disable weekly report email notifications.

Bulk Edit Time Entries

Tool to bulk edit multiple time entries in a workspace using JSON Patch operations.

Get All Plans

Tool to retrieve all available Toggl subscription plans and their features.

Get Client Details

Retrieves detailed information about a specific client in Toggl Track by its client ID and workspace ID.

Get Countries

Tool to retrieve all countries supported by Toggl.

Get Country Subdivisions

Tool to retrieve all subdivisions (states, provinces, regions) for a specific country in Toggl Track.

Get Currencies

Tool to retrieve the list of all currencies supported by Toggl Track.

Get Current Time Entry

Retrieves the currently running time entry for the authenticated user.

Get Event Filters

Retrieve the list of supported event filters for Toggl webhooks.

Get JWKS Keys

Retrieves the current JWKS (JSON Web Key Set) keyset used to sign JWT tokens.

List Clients

Retrieve a list of clients from a Toggl Track workspace with optional filtering by status and name.

Get My Location

Retrieves the authenticated user's last known location information including city, state, country, and coordinates.

Get My Quota

Tool to retrieve API rate limit quota for the authenticated user.

Get Organization Details

Retrieves detailed information about a specific Toggl organization including subscription plan, trial status, user count, and workspace settings.

Get Organization Groups

Retrieves all groups within a Toggl organization, including group members and workspace assignments.

Get Organization Users

Retrieves a list of users belonging to a Toggl organization.

Get Project Details

Tool to retrieve details of a specific project.

Get Projects

Tool to retrieve a list of projects from a Toggl workspace.

Get Public Subscription Plans

Tool to retrieve all publicly available subscription plans from Toggl.

Get Webhooks Status

Tool to retrieve the Toggl Webhooks server status.

Get Tags

Retrieve all tags in a Toggl workspace.

List Tasks

Tool to list tasks in a workspace or within a specific project.

Get Time Entries

Retrieve time entries for the authenticated user with flexible filtering options.

Get Time Entry

Tool to retrieve a specific time entry by its ID.

Get Timezone Offsets

Tool to retrieve all available timezone offsets from Toggl.

Get Timezones

Tool to retrieve all available timezones supported by Toggl Track.

Get User Clients

Retrieves all clients accessible to the authenticated user across all their workspaces.

Get User Preferences

Retrieves the authenticated user's preferences including timezone, date/time formats, notification settings, and enabled alpha/experimental features.

Get User Projects

Tool to retrieve all projects for the authenticated user.

Get User Tags

Tool to retrieve tags associated with the current user.

Get User Tasks

Retrieve all tasks across all workspaces accessible to the authenticated user.

Get User Workspaces

Tool to retrieve all workspaces the authenticated user belongs to.

Get Workspace Details

Retrieves comprehensive details and settings for a specific Toggl workspace by ID.

Get Workspace Logo

Tool to get workspace logo.

Get Workspace Preferences

Retrieves workspace preferences including the initial pricing plan and whether start/end times are hidden.

Get Workspace Users

Retrieves all users who belong to a specific Toggl workspace.

Stop Time Entry

Tool to stop a running time entry in a workspace.

Disable Product Emails

Tool to disable product emails for the authenticated user using a disable code.

Update Tag

Tool to update an existing tag in a specified workspace.

Send Demo Email

Tool to send a demo request email through Toggl's system.

Send Email to Contact

Tool to send an email to a contact via Toggl's smail service.

Send Smail Meet

Tool to send an email for meet.

Update Client

Updates an existing client in a Toggl workspace.

FRAMEWORKS

How to build Toggl MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Toggl MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Toggl MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Toggl MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Toggl MCP with Claude Cowork

Codex logo

Codex

Use Toggl MCP with Codex

OpenClaw logo

OpenClaw

Use Toggl MCP with OpenClaw

Hermes logo

Hermes

Use Toggl MCP with Hermes

CLI logo

CLI

Use Toggl MCP with CLI

Google ADK logo

Google ADK

Use Toggl MCP with Google ADK

LangChain logo

LangChain

Use Toggl MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Toggl MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Toggl MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Toggl MCP with LlamaIndex

CrewAI logo

CrewAI

Use Toggl MCP with CrewAI

FAQ

Frequently asked questions

With a standalone Toggl MCP server, the agents and LLMs can only access a fixed set of Toggl tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Toggl and many other apps based on the task at hand, all through a single MCP endpoint.

Yes, you can. Autogen 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 Toggl tools.

Yes, absolutely. You can configure which Toggl 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.

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 Toggl data and credentials are handled as safely as possible.

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A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

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Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

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