Scheduler MCP Server

Overview

MCP Scheduler is a lightweight, cross‑platform task automation server built on the Model Context Protocol. It addresses the common developer pain point of coordinating time‑based actions across disparate systems—whether that means running a nightly data pipeline, pinging an external API, or prompting an AI model to generate content at a set interval. By exposing its capabilities through MCP, the scheduler can be discovered and invoked by any AI assistant or tool that understands the protocol, turning a simple cron job into an intelligent, context‑aware service.

At its core, the server manages four distinct task types: shell commands, API calls, AI content generation, and desktop reminders. Each task is defined with a cron expression, allowing for the full flexibility of standard cron syntax while still being fully describable in JSON. When a task triggers, the scheduler records its execution—success or failure—into an SQLite database, providing a complete audit trail that developers can query or visualize later. The history feature is especially valuable for debugging long‑running jobs or for compliance purposes where auditability matters.

For developers integrating AI assistants, MCP Scheduler offers a clean set of resources and tools. An AI assistant can query the scheduler’s resource list to discover available tasks, invoke a task by name, or even schedule new ones on the fly. Because the scheduler’s interface is MCP‑native, no custom client code is required; a standard MCP library can talk to it over either stdin/stdout or Server‑Sent Events, depending on the deployment scenario. This makes the scheduler an ideal companion for AI workflows that need to trigger external processes—such as sending a notification after an LLM has finished generating a report, or automatically backing up data before a model retrain.

Unique advantages include its multi‑type support—developers can keep all their scheduled operations in one place rather than juggling separate cron files, Docker containers, or cloud functions. The desktop reminder feature turns the scheduler into a personal assistant, popping up notifications with sound to remind developers of meetings or deadlines. Robust error handling and detailed logging mean that failed tasks surface quickly, reducing downtime in production environments.

In real‑world scenarios, MCP Scheduler shines for continuous integration pipelines that need to trigger downstream services, for data scientists who want to schedule model retraining jobs, or for teams that use AI assistants to manage day‑to‑day operations. By integrating seamlessly with MCP‑compatible clients, it becomes a first‑class citizen in any AI‑driven ecosystem, turning routine scheduling into an extensible, auditable service that anyone can consume.