Toggl Track MCP Server

Overview

The Toggl Track MCP Server bridges the gap between AI assistants and real‑world time‑tracking data. By exposing a rich set of tools that mirror the Toggl Track API, it lets Claude and other MCP clients read, manipulate, and report on projects, workspaces, time entries, and timers directly from a conversation. This eliminates the need for developers to write custom integration code, enabling rapid prototyping of productivity workflows that combine natural language understanding with actionable time‑tracking commands.

What Problem Does It Solve?

Time tracking is a ubiquitous requirement for freelancers, agencies, and product teams. Traditionally, users must manually log hours in Toggl’s web interface or use a dedicated desktop app. For developers building AI‑powered assistants, this manual step breaks the conversational flow and forces users to switch contexts. The Toggl Track MCP Server turns time‑tracking into a first‑class conversational capability: users can ask, “How many hours did I spend on Project X last week?” or “Start a timer for the ‘Code Review’ task,” and receive instant, accurate responses without leaving their chat.

Core Value for AI Workflows

The server’s toolset is designed around the most common time‑tracking interactions. Developers can embed these tools into larger AI pipelines, such as automated reporting bots or project management assistants that trigger timers when a new issue is opened. Because the server formats output for LLM consumption, developers can chain multiple tools—retrieving a project list, filtering entries, and summarizing time—all within a single prompt. This tight integration keeps the assistant’s state in sync with Toggl, ensuring that suggestions and reminders are based on up‑to‑date data.

Key Features Explained

• Project & Workspace Discovery – and return structured lists that include metadata like client names, colors, and privacy flags, allowing assistants to build context‑aware menus.
• Time Entry Management – supports date range and project filtering, grouping entries by day for clear summaries.
• Aggregated Reporting – aggregates hours per project and calculates percentages, enabling quick visual dashboards.
• Timer Control – , , and give full control over Toggl’s timer, making it possible to start a new session or pause an existing one via chat.
• Task Handling – and let users manage project tasks with time estimates, integrating task management into the conversational loop.
• Search & Smart Prompts – and pre‑built prompts help users locate specific entries or initiate common queries without memorizing syntax.

Real‑World Use Cases

• Productivity Coaching Bots – Prompt users to start timers when a new feature is added, then later summarize weekly effort.
• Project Management Dashboards – Generate real‑time reports on hours spent per client, feeding data into Slack or Teams channels.
• Automated Invoicing Helpers – Pull time summaries for billing periods and format them into invoice templates.
• Developer Onboarding – Teach new team members how to log time by having the assistant walk them through each step in natural language.

Standout Advantages

• Zero‑Code Integration – Because the server is an MCP, developers need only configure a single JSON entry; no custom API wrappers are required.
• Secure Token Handling – The server accepts the Toggl API token via environment variables, keeping credentials out of code repositories.
• LLM‑Friendly Output – All responses are human‑readable, enabling Claude to present polished information without additional formatting logic.
• Extensibility – The tool list can be expanded with new endpoints or custom prompts, allowing teams to tailor the assistant’s capabilities to their workflows.

In summary, the Toggl Track MCP Server transforms a conventional time‑tracking tool into an interactive AI resource. By exposing Toggl’s core functionality through conversational commands, it empowers developers to build smarter assistants that keep users productive without leaving the chat.