Lazy Toggl MCP Server

The Lazy Toggl MCP Server bridges the gap between AI assistants and the Toggl Track time‑tracking platform. By exposing a set of well‑defined tools over the Model Context Protocol, it lets conversational agents start, stop, and query time entries without requiring developers to write custom API wrappers. This solves a common pain point for teams that rely on AI for productivity workflows: integrating real‑world task tracking into a single, seamless conversation.

At its core, the server offers four primary tools. initiates a new Toggl entry, accepting optional parameters such as workspace, project, and tags to match Toggl’s data model. halts the currently running entry, returning a confirmation to the assistant. retrieves all workspaces linked to the user, enabling dynamic workspace selection in dialogue. provides a snapshot of any active entry, including its description, start time, duration, and associated metadata. These tools map directly to Toggl’s v9 API endpoints, ensuring that the server remains lightweight while offering full functionality.

Developers benefit from a clean separation of concerns: the MCP server handles authentication (via an environment‑set API token), HTTP communication, and data transformation, while the AI model focuses on intent extraction and dialogue flow. Integrating Lazy Toggl MCP into an existing workflow simply involves adding the server’s configuration to the AI assistant’s settings file; no code changes are required in the agent itself. The server’s field can be left empty, allowing the assistant to request permission for each tool invocation—an approach that balances usability with security.

Real‑world scenarios illustrate its value. A project manager can ask the assistant to “start tracking my sprint planning session,” and the server will create a Toggl entry with the appropriate tags. During a stand‑up, a developer might request “stop my current timer” or “show me what I'm currently tracking,” receiving instant feedback. Teams using multiple workspaces can dynamically switch contexts by listing and selecting the desired workspace before starting a new entry. Because all interactions are routed through MCP, these operations can be embedded in longer conversations or triggered by voice commands in virtual assistants.

Lazy Toggl MCP’s standout feature is its zero‑code integration model. By adhering strictly to the MCP specification, it can be dropped into any Claude or other AI assistant that supports external tools. The server’s lightweight Python implementation, coupled with a clear configuration schema, makes onboarding fast and repeatable. This combination of ease of use, direct Toggl API coverage, and strict adherence to MCP standards gives developers a powerful tool for embedding time‑tracking into AI‑driven productivity pipelines.