How to integrate Toggl Track MCP with CrewAI

This guide walks you through connecting Toggl Track to CrewAI using the Composio tool router. By the end, you'll have a working Toggl Track agent that can track your time, create clients and projects, manage your tasks, and update time entries through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Toggl Track account through Composio's Toggl Track MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Toggl Track

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Toggl Track is a time tracking platform for recording work, organizing projects, and managing workspaces. It helps teams understand where time goes with clear reports, billable hours, and lightweight workflows.

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Introduction

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

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

Also integrate Toggl Track with

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

TL;DR

Here's what you'll learn:

Get a Composio API key and configure your Toggl Track connection
Set up CrewAI with an MCP enabled agent
Create a Tool Router session or standalone MCP server for Toggl Track
Build a conversational loop where your agent can execute Toggl Track operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

Agent Roles: Define specialized agents with specific goals and backstories
Task Management: Create tasks with clear descriptions and expected outputs
Crew Orchestration: Combine agents and tasks into collaborative workflows
MCP Integration: Connect to external tools through Model Context Protocol

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

The Toggl Track 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 Track account. It provides structured and secure access to your time entries, workspaces, projects, clients, tasks, and reports, so your agent can track work, manage timers, organize projects, update records, and produce time reports on your behalf.

Time entry management: Have your agent create, review, update, or delete time entries, including their descriptions, timing, billable status, tags, and work assignments.
Running timer control: Let the agent check your current timer, start a new running time entry, or stop work in progress.
Workspace and project organization: Direct your agent to list workspaces and manage projects, clients, and tasks used to organize tracked work.
Detailed time reporting: Instruct your agent to produce detailed time reports for a workspace and date range, grouped into useful report rows.
Bulk record updates: Have your agent apply the same description or billable status to multiple time entries at once.

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

Step by step08 STEPS

Prerequisites

Before starting, make sure you have:

Python 3.9 or higher
A Composio account and API key
A Toggl Track connection authorized in Composio
An OpenAI API key for the CrewAI LLM
Basic familiarity with Python

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 crewai crewai-tools[mcp] python-dotenv

What's happening:

composio connects your agent to Toggl Track via MCP
crewai provides Agent, Task, Crew, and LLM primitives
crewai-tools[mcp] includes MCP helpers
python-dotenv loads environment variables from .env

Set up environment variables

bash

COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

COMPOSIO_API_KEY authenticates with Composio
USER_ID scopes the session to your account
OPENAI_API_KEY lets CrewAI use your chosen OpenAI model

Import dependencies

python

import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")

What's happening:

CrewAI classes define agents and tasks, and run the workflow
MCPServerHTTP connects the agent to an MCP endpoint
Composio will give you a short lived Toggl Track MCP URL

Create a Composio Tool Router session for Toggl Track

python

composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["toggl_track"])

url = session.mcp.url

What's happening:

You create a Toggl Track only session through Composio
Composio returns an MCP HTTP URL that exposes Toggl Track tools

Initialize the MCP Server

python

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )

What's Happening:

Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.

Create a CLI Chatloop and define the Crew

python

print("Chat started! Type 'exit' or 'quit' to end.\n")

conversation_context = ""

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], verbose=False)
    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")

What's Happening:

Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.

Complete Code

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

python

from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["toggl_track"],
)
url = session.mcp.url

# Configure LLM
llm = LLM(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY"),
)

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\n")

    conversation_context = ""

    while True:
        user_input = input("You: ").strip()

        if user_input.lower() in ["exit", "quit", "bye"]:
            print("\nGoodbye!")
            break

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

Conclusion

You now have a CrewAI agent connected to Toggl Track through Composio's Tool Router. The agent can perform Toggl Track operations through natural language commands.

Next steps:

Add role-specific instructions to customize agent behavior
Plug in more toolkits for multi-app workflows
Chain tasks for complex multi-step operations

TOOLS

Supported Tools

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

Bulk Update Time Entries

Apply the same description and/or billable state to 1 to 100 Toggl Track time entries in one provider-native request.

Create Client

Create a client in a workspace for grouping projects.

Create Project

Create a project in a workspace for organizing time entries and tasks.

Create Task

Create a task under an active project for more specific time-entry assignment.

Create Time Entry

Create a running or completed time entry in a workspace; omit duration and stop to start a running timer.

Delete Client

Permanently delete one client from a Toggl Track workspace.

Delete Project

Delete one project while preserving its time entries by unassigning them from the project.

Delete Task

Permanently delete one task from its parent project.

Delete Time Entry

Permanently delete one time entry from a workspace.

Get Current Time Entry

Return the currently running time entry for the connected user, or indicate that no timer is running.

Get Current User

Return the connected Toggl Track user's identity and defaults, including the default workspace ID, without exposing credentials.

Get Time Entry

Get one time entry by ID, with optional related-entity and sharing metadata.

List Time Entries

List the connected user's time entries in an explicit date range, optionally including related project, task, user, and sharing metadata.

List Workspace Resources

Find projects, clients, tags, or tasks in a workspace and return their IDs for time-entry and resource-management tools.

List Workspaces

List workspaces accessible to the connected user and return the IDs needed by workspace-scoped tools, without exposing private tokens or calendar URLs.

Run Detailed Time Report

Return one page of detailed, grouped time-entry report rows for a workspace and date range, with an opaque cursor for the next page.

Stop Time Entry

Stop a currently running time entry and return its finalized timing data.

Update Client

Update a client's name, notes, or external reference.

Update Project

Update a project's name, client, active state, privacy, color, or date range.

Update Task

Update a task's name, completion state, estimate, assignee, or external reference.

Update Time Entry

Update the timing, description, assignment, billable state, or tags of an existing time entry.

FRAMEWORKS

How to build Toggl Track MCP Agent with another framework

OpenAI Agents SDK

Use Toggl Track MCP with OpenAI Agents SDK

Claude Agent SDK

Use Toggl Track MCP with Claude Agent SDK

Claude Code

Use Toggl Track MCP with Claude Code

Claude Cowork

Use Toggl Track MCP with Claude Cowork

Codex

Use Toggl Track MCP with Codex

OpenClaw

Use Toggl Track MCP with OpenClaw

Hermes

Use Toggl Track MCP with Hermes

CLI

Use Toggl Track MCP with CLI

Google ADK

Use Toggl Track MCP with Google ADK

LangChain

Use Toggl Track MCP with LangChain

Vercel AI SDK

Use Toggl Track MCP with Vercel AI SDK

Mastra AI

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LlamaIndex

Use Toggl Track MCP with LlamaIndex

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Breeze

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FAQ

Frequently asked questions

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

Yes, you can. CrewAI 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 Track tools.

Yes, absolutely. You can configure which Toggl Track 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 Track data and credentials are handled as safely as possible.

Start with Toggl Track.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Toggl Track tool your agent needs.Free to start.

Start building

Customer stories

Teams ship real agents on Composio

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

Harsha Gaddipati

Co-founder, Slashy

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.

Abhi Arya

Co-founder, Opennote

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

Nirman Dave

CEO, Zams

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

Ryan Yu

Founder, Extra Thursday

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.

Tomisin Jenrola

Founder & CEO, SwarmZero

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.

Jerome Leclanche

Co-Founder, Ingram Technologies

How to integrate Toggl Track MCP with CrewAI

Connect Toggl Track without auth hassles

Introduction

Also integrate Toggl Track with

TL;DR

What is CrewAI?

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

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

Create a Composio Tool Router session for Toggl Track

Initialize the MCP Server

Create a CLI Chatloop and define the Crew

Complete Code

Conclusion

Supported Tools

How to build Toggl Track 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

Explore Other Toolkits

Amazing Marvin

ApptiveGrid

Boxhero

Breeze

Frequently asked questions

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

Can I use Tool Router MCP with CrewAI?+

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

How safe is my data with Composio Tool Router?+

Start with Toggl Track.It takes 30 seconds.

Teams ship real agents on Composio