Simple Timer MCP Server

The Simple Timer MCP Server addresses a common need in AI‑augmented workflows: the ability to track elapsed time without leaving the assistant’s environment. In many development, research, or creative scenarios, an AI helper may need to remember how long a task has been running, whether a code review is overdue, or when a user‑initiated process should be refreshed. Rather than relying on external schedulers or manual logging, this server exposes a lightweight, token‑based timer system that can be queried and managed entirely through MCP tools.

At its core, the server offers four primary actions: , , , and . A developer can initiate a timer by providing a unique token string; the server records the start timestamp in an embedded SQLite database, ensuring persistence across restarts. When the timer is queried, the server calculates elapsed time and can return it in either raw milliseconds or a human‑readable format such as “2 hours, 15 minutes ago.” This dual representation is valuable for both programmatic decision‑making and natural language explanations. The ability to delete timers or list all active ones gives full lifecycle control, making it suitable for batch processes or cleanup routines.

The design prioritizes simplicity and reliability. By using a local SQLite database, the server avoids external dependencies while still offering durable storage for timer data. The implementation is intentionally minimalistic, making it an excellent teaching tool for developers new to MCP who want to understand how to expose stateful operations as tools. The server communicates via the standard , which means it can be launched as a child process from any MCP‑compatible host such as Claude Code, Cursor, or custom tooling.

In real‑world use cases, this timer server can power features like:

• Time‑based reminders: An AI assistant could schedule a prompt to remind the user after a set interval.
• Performance monitoring: Track how long build or test pipelines run within an AI‑driven CI/CD workflow.
• Session management: Keep track of user inactivity or session timeouts in conversational AI applications.
• Experiment tracking: Record the duration of machine learning training jobs or data processing steps.

Because all interactions are encapsulated as MCP tools, developers can seamlessly integrate the timer into existing prompts or workflows. For example, an assistant could automatically start a timer when a code review is opened and later check the elapsed time to decide whether to prompt the reviewer for an update. The server’s lightweight nature ensures that adding this capability does not introduce significant overhead, making it a practical addition to any AI‑enhanced development environment.