Toggl with AutoGen

How to integrate Toggl MCP with Autogen

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Introduction Also integrate Toggl with TL;DR What is AutoGen What is the Toggl MCP server Supported Tools & Triggers Creating MCP Server - Stand-alone vs Composio SDK Step-by-step Guide Complete Code Conclusion How to build Toggl MCP Agent with another framework Explore Other Toolkits FAQ

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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 SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenClaw Hermes CLI Google ADK LangChain Vercel AI SDK Mastra AI LlamaIndex CrewAI

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.

Supported Tools & Triggers

Tools

Create ClientTool to create a new client in a workspace.

Create GroupTool to create a new group in a Toggl organization.

Create InvitationTool to send invitations to join a Toggl organization.

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

Create ProjectCreates a new project in a Toggl workspace.

Create TagTool to create a new tag in a workspace.

Create Time EntryTool to create a new time entry in the specified workspace.

Add User to Workspace ProjectTool to add a user to workspace project users.

Delete Toggl ClientTool to delete a client in Toggl.

Delete GroupTool to delete a group from a Toggl organization.

Delete Project GroupTool to delete a project group from a Toggl workspace.

Delete SubscriptionTool to delete a webhook subscription in Toggl.

Delete TagDeletes a tag from a Toggl workspace.

Disable Weekly ReportTool to disable weekly report email notifications.

Bulk Edit Time EntriesTool to bulk edit multiple time entries in a workspace using JSON Patch operations.

Get All PlansTool to retrieve all available Toggl subscription plans and their features.

Get Client DetailsRetrieves detailed information about a specific client in Toggl Track by its client ID and workspace ID.

Get CountriesTool to retrieve all countries supported by Toggl.

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

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

Get Current Time EntryRetrieves the currently running time entry for the authenticated user.

Get Event FiltersRetrieve the list of supported event filters for Toggl webhooks.

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

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

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

Get My QuotaTool to retrieve API rate limit quota for the authenticated user.

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

Get Organization GroupsRetrieves all groups within a Toggl organization, including group members and workspace assignments.

Get Organization UsersRetrieves a list of users belonging to a Toggl organization.

Get Project DetailsTool to retrieve details of a specific project.

Get ProjectsTool to retrieve a list of projects from a Toggl workspace.

Get Public Subscription PlansTool to retrieve all publicly available subscription plans from Toggl.

Get Webhooks StatusTool to retrieve the Toggl Webhooks server status.

Get TagsRetrieve all tags in a Toggl workspace.

List TasksTool to list tasks in a workspace or within a specific project.

Get Time EntriesRetrieve time entries for the authenticated user with flexible filtering options.

Get Time EntryTool to retrieve a specific time entry by its ID.

Get Timezone OffsetsTool to retrieve all available timezone offsets from Toggl.

Get TimezonesTool to retrieve all available timezones supported by Toggl Track.

Get User ClientsRetrieves all clients accessible to the authenticated user across all their workspaces.

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

Get User ProjectsTool to retrieve all projects for the authenticated user.

Get User TagsTool to retrieve tags associated with the current user.

Get User TasksRetrieve all tasks across all workspaces accessible to the authenticated user.

Get User WorkspacesTool to retrieve all workspaces the authenticated user belongs to.

Get Workspace DetailsRetrieves comprehensive details and settings for a specific Toggl workspace by ID.

Get Workspace LogoTool to get workspace logo.

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

Get Workspace UsersRetrieves all users who belong to a specific Toggl workspace.

Stop Time EntryTool to stop a running time entry in a workspace.

Disable Product EmailsTool to disable product emails for the authenticated user using a disable code.

Update TagTool to update an existing tag in a specified workspace.

Send Demo EmailTool to send a demo request email through Toggl's system.

Send Email to ContactTool to send an email to a contact via Toggl's smail service.

Send Smail MeetTool to send an email for meet.

Update ClientUpdates an existing client in a Toggl workspace.

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:

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

Step-by-step Guide

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

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.

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

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

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

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

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

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.

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TOOLKIT MARKETPLACE

FAQ

What are the differences in Tool Router MCP and Toggl MCP?

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.

Can I use Tool Router MCP with Autogen?

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.

Can I manage the permissions and scopes for Toggl while using Tool Router?

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.

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 Toggl data and credentials are handled as safely as possible.

Used by agents from

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We handle tool reliability, observability, and security so you never have to second-guess an agent action.

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Harsha GaddipatiCo-founder, Slashy

Karan skipped his own birthday party to fix our critical issue. It was 10 pm and he diverted his Waymo to help us instead. This really sets the bar, shows you the commitment you need to have when users rely on your software.

Abhi AryaCo-founder, Opennote

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 has immensely helped in keeping new users on the platform.

Nirman DaveCEO, Zams

We chose Composio over Pipedream because it delivered depth where it mattered. It supported niche tools and tricky edge cases that other platforms simply ignored. Giving us confidence to scale without compromising.

Ryan YuFounder, Extra Thursday

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down in the complexities of managing agent auth would have been a death sentence for Extra Thursday.

Tomisin JenrolaFounder & CEO, SwarmZero

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

Jerome LeclancheCo-Founder, Ingram Technologies

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Harsha GaddipatiCo-founder, Slashy

Abhi AryaCo-founder, Opennote

Nirman DaveCEO, Zams

Ryan YuFounder, Extra Thursday

Tomisin JenrolaFounder & CEO, SwarmZero

Jerome LeclancheCo-Founder, Ingram Technologies

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Harsha GaddipatiCo-founder, Slashy

Abhi AryaCo-founder, Opennote

Nirman DaveCEO, Zams

Ryan YuFounder, Extra Thursday

Tomisin JenrolaFounder & CEO, SwarmZero

Jerome LeclancheCo-Founder, Ingram Technologies

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

How to integrate Toggl MCP with Autogen

Table of Contents

Connect Toggl without Auth hassles

Introduction

Also integrate Toggl with

TL;DR

What is AutoGen?

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

Supported Tools & Triggers

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

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import dependencies and create Tool Router session

Configure MCP parameters for Autogen

Create the model client and agent

Run the interactive chat loop

Complete Code

Conclusion

How to build Toggl MCP Agent with another framework

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

OpenClaw

Hermes

CLI

Google ADK

LangChain

Vercel AI SDK

Mastra AI

LlamaIndex

CrewAI

Explore Other Toolkits

Google Sheets

Notion

Airtable

FAQ

What are the differences in Tool Router MCP and Toggl MCP?

Can I use Tool Router MCP with Autogen?

Can I manage the permissions and scopes for Toggl while using Tool Router?

How safe is my data with Composio Tool Router?

Used by agents from

Never worry about agent reliability