How to integrate Toggl Track MCP with Mastra AI

This guide walks you through connecting Toggl Track to Mastra AI 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 Mastra AI 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 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.

21 Tools

Introduction

This guide walks you through connecting Toggl Track to Mastra AI 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 Mastra AI 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 SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKLlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Set up your environment so Mastra, OpenAI, and Composio work together
  • Create a Tool Router session in Composio that exposes Toggl Track tools
  • Connect Mastra's MCP client to the Composio generated MCP URL
  • Fetch Toggl Track tool definitions and attach them as a toolset
  • Build a Mastra agent that can reason, call tools, and return structured results
  • Run an interactive CLI where you can chat with your Toggl Track agent

What is Mastra AI?

Mastra AI is a TypeScript framework for building AI agents with tool support. It provides a clean API for creating agents that can use external services through MCP.

Key features include:

  • MCP Client: Built-in support for Model Context Protocol servers
  • Toolsets: Organize tools into logical groups
  • Step Callbacks: Monitor and debug agent execution
  • OpenAI Integration: Works with OpenAI models via @ai-sdk/openai

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:

  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 step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Node.js 18 or higher
  • A Composio account with an active API key
  • An OpenAI API key
  • Basic familiarity with TypeScript
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key.
  • You need credits or a connected billing setup to use the models.
  • Store the key somewhere safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Go to Settings and copy your API key.
  • This key lets your Mastra agent talk to Composio and reach Toggl Track through MCP.
3

Install dependencies

bash
npm install @composio/core @mastra/core @mastra/mcp @ai-sdk/openai dotenv

Install the required packages.

What's happening:

  • @composio/core is the Composio SDK for creating MCP sessions
  • @mastra/core provides the Agent class
  • @mastra/mcp is Mastra's MCP client
  • @ai-sdk/openai is the model wrapper for OpenAI
  • dotenv loads environment variables from .env
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_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 your requests to Composio
  • COMPOSIO_USER_ID tells Composio which user this session belongs to
  • OPENAI_API_KEY lets the Mastra agent call OpenAI models
5

Import libraries and validate environment

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) 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 as string,
});
What's happening:
  • dotenv/config auto loads your .env so process.env.* is available
  • openai gives you a Mastra compatible model wrapper
  • Agent is the Mastra agent that will call tools and produce answers
  • MCPClient connects Mastra to your Composio MCP server
  • Composio is used to create a Tool Router session
6

Create a Tool Router session for Toggl Track

typescript
async function main() {
  const session = await composio.create(
    composioUserID as string,
    {
      toolkits: ["toggl_track"],
    },
  );

  const composioMCPUrl = session.mcp.url;
  console.log("Toggl Track MCP URL:", composioMCPUrl);
What's happening:
  • create spins up a short-lived MCP HTTP endpoint for this user
  • The toolkits array contains "toggl_track" for Toggl Track access
  • session.mcp.url is the MCP URL that Mastra's MCPClient will connect to
7

Configure Mastra MCP client and fetch tools

typescript
const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      nasdaq: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

console.log("Fetching MCP tools from Composio...");
const composioTools = await mcpClient.getTools();
console.log("Number of tools:", Object.keys(composioTools).length);
What's happening:
  • MCPClient takes an id for this client and a list of MCP servers
  • The headers property includes the x-api-key for authentication
  • getTools fetches the tool definitions exposed by the Toggl Track toolkit
8

Create the Mastra agent

typescript
const agent = new Agent({
    name: "toggl_track-mastra-agent",
    instructions: "You are an AI agent with Toggl Track tools via Composio.",
    model: "openai/gpt-5",
  });
What's happening:
  • Agent is the core Mastra agent
  • name is just an identifier for logging and debugging
  • instructions guide the agent to use tools instead of only answering in natural language
  • model uses openai("gpt-5") to configure the underlying LLM
9

Set up interactive chat interface

typescript
let messages: AiMessageType[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end.\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({
    id: crypto.randomUUID(),
    role: "user",
    content: trimmedInput,
  });

  console.log("\nAgent is thinking...\n");

  try {
    const response = await agent.generate(messages, {
      toolsets: {
        toggl_track: composioTools,
      },
      maxSteps: 8,
    });

    const { text } = response;

    if (text && text.trim().length > 0) {
      console.log(`Agent: ${text}\n`);
        messages.push({
          id: crypto.randomUUID(),
          role: "assistant",
          content: text,
        });
      }
    } catch (error) {
      console.error("\nError:", error);
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    console.log("\nSession ended.");
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});
What's happening:
  • messages keeps the full conversation history in Mastra's expected format
  • agent.generate runs the agent with conversation history and Toggl Track toolsets
  • maxSteps limits how many tool calls the agent can take in a single run
  • onStepFinish is a hook that prints intermediate steps for debugging

Complete Code

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

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) 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 as string });

async function main() {
  const session = await composio.create(composioUserID as string, {
    toolkits: ["toggl_track"],
  });

  const composioMCPUrl = session.mcp.url;

  const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      toggl_track: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "toggl_track-mastra-agent",
    instructions: "You are an AI agent with Toggl Track tools via Composio.",
    model: "openai/gpt-5",
  });

  let messages: AiMessageType[] = [];

  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: "> ",
  });

  rl.prompt();

  rl.on("line", async (input: string) => {
    const trimmed = input.trim();
    if (["exit", "quit"].includes(trimmed.toLowerCase())) {
      rl.close();
      return;
    }

    messages.push({ id: crypto.randomUUID(), role: "user", content: trimmed });

    const { text } = await agent.generate(messages, {
      toolsets: { toggl_track: composioTools },
      maxSteps: 8,
    });

    if (text) {
      console.log(`Agent: ${text}\n`);
      messages.push({ id: crypto.randomUUID(), role: "assistant", content: text });
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main();

Conclusion

You've built a Mastra AI agent that can interact with Toggl Track through Composio's Tool Router. You can extend this further by:
  • Adding other toolkits like Gmail, Slack, or GitHub
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows
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 logo

OpenAI Agents SDK

Use Toggl Track MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Toggl Track MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Toggl Track MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Toggl Track MCP with Claude Cowork

Codex logo

Codex

Use Toggl Track MCP with Codex

OpenClaw logo

OpenClaw

Use Toggl Track MCP with OpenClaw

Hermes logo

Hermes

Use Toggl Track MCP with Hermes

CLI logo

CLI

Use Toggl Track MCP with CLI

Google ADK logo

Google ADK

Use Toggl Track MCP with Google ADK

LangChain logo

LangChain

Use Toggl Track MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Toggl Track MCP with Vercel AI SDK

LlamaIndex logo

LlamaIndex

Use Toggl Track MCP with LlamaIndex

CrewAI logo

CrewAI

Use Toggl Track MCP with CrewAI

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. Mastra AI 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.

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