MCP Cron

MCP Cron is a Model Context Protocol server designed to bring reliable, time‑driven task execution into AI workflows. By exposing a standard API that accepts cron expressions, it allows both traditional shell commands and AI‑driven prompts to be scheduled, monitored, and managed from any MCP‑compatible client. This solves the common pain point of orchestrating background jobs—such as nightly data pulls, periodic report generation, or routine system maintenance—without having to rely on external schedulers like cron or Airflow.

The server’s core value lies in its dual‑mode transport. The default SSE (Server‑Sent Events) channel provides a lightweight HTTP stream that integrates seamlessly with web browsers and networked clients, while the stdio mode enables direct piping between processes, making it ideal for desktop AI assistants that lack native SSE support. This flexibility means developers can embed the scheduler directly into their existing tooling stack, whether they are building a CLI utility, a containerized microservice, or a desktop application.

Key capabilities include:

• Cron‑based scheduling for both shell commands and AI prompts, giving developers granular control over execution timing.
• Task management via the MCP protocol: create, update, delete, and query scheduled jobs without leaving the AI interface.
• Output capture that returns command results back to the caller, allowing subsequent tools or prompts to consume the data immediately.
• AI task execution powered by a configurable LLM (defaulting to GPT‑4o), enabling the scheduler to trigger complex, multi‑step AI workflows on a timetable.

Typical use cases span data engineering (e.g., nightly ETL jobs), DevOps automation (periodic health checks or log rotation), and content generation (scheduled blog post drafts). In each scenario, the scheduler removes manual intervention, ensures repeatability, and provides a single source of truth that AI assistants can query to stay up‑to‑date with system state.

Integrating MCP Cron into an AI workflow is straightforward: a client registers the server in its configuration, then uses standard MCP tool calls to create or modify jobs. Because the scheduler itself is an MCP server, any client—whether a web app, IDE extension, or desktop assistant—can manage tasks using the same protocol. This uniformity simplifies onboarding for developers and reduces friction when scaling AI‑augmented automation across teams.