What are the prerequisites for MCP Scheduler?
Python 3.10 or higher and either uv (recommended) or standard pip are required.
@PhialsBasement
Basic information
MCP Scheduler is a task automation server that lets you schedule shell commands, API calls, AI tasks, and desktop notifications using cron expressions. Built with Model Context Protocol for seamless integration with Claude Desktop and other AI assistants.
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"scheduler-mcp": {
"command": "uv",
"args": [
"venv"
]
}
}
}Tool list not available yet
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We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
MCP Scheduler is a robust task scheduler server built with the Model Context Protocol (MCP) for scheduling and managing automated tasks. It supports shell commands, API calls, AI content generation via OpenAI, and desktop reminders with sound. The server uses cron expressions for flexible timing and provides a complete execution history.
Install via uv or standard pip, then run python main.py with optional arguments (e.g., --transport sse --port 8080). Integrate with MCP clients like Claude Desktop by adding the server to its configuration file. Tasks are added via MCP tools such as add_command_task, add_api_task, add_ai_task, and add_reminder_task, each requiring a cron schedule and optional do_only_once flag.
Python 3.10 or higher and either uv (recommended) or standard pip are required.
Configuration can be done via command-line arguments (e.g., `--port`, `--db-path`), a JSON configuration file, or environment variables (e.g., `MCP_SCHEDULER_PORT`, `OPENAI_API_KEY`).
The server supports SSE (Server-Sent Events) and stdio (standard input/output) transport modes.
Provide an OpenAI API key via the `OPENAI_API_KEY` environment variable or the configuration file, and optionally set the AI model with `--ai-model` (default `gpt-4o`).
Yes, set the `do_only_once` parameter to `True` when adding a task, and it will execute only once at the scheduled time.

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