home / mcp / homra heartbeat mcp server
Provides a Python-backed MCP server with a persistent config and a dynamic scheduler, plus a React dashboard for remote control.
Configuration
View docs{
"mcpServers": {
"fadedchange0aran-netizen-homraheartbeatai": {
"command": "python",
"args": [
"server.py"
]
}
}
}You have a complete MCP server that runs a Python backend to manage APIs, persist configurations, and schedule tasks, plus a React dashboard to remotely update settings and trigger actions. This server is designed for convenient control and automation from a central management interface, with a persistent config and dynamic scheduling that can be adjusted without restarting the service.
To use this MCP server, run the local Python backend on your VPS or server and connect your MCP client to the server as configured. The server exposes a management interface on the default port and supports live configuration syncing from the dashboard.
Prerequisites you need on your server: Python 3.x, and network access to install Python dependencies. You will also need Git to clone the project and pip to install dependencies.
# Clone the repository
git clone https://github.com/YOUR_USER/homra-heartbeat.git
cd homra-heartbeat
# Install dependencies
pip install -r requirements.txt
# Start the server
python server.py
```} ]},{Notes about running: the server uses port 8000 by default. The first run will automatically create a config.json file. You can access the management interface at http://YOUR_IP:8000/ to view and manage settings.
Configuration and runtime behavior are centralized in config.json on the server’s local filesystem. Changes you make in the dashboard are saved to this file and take effect dynamically without requiring a full server restart.
Auto-synchronization happens when the frontend loads and whenever you push changes from the dashboard. The backend reloads timers and applies the new configuration immediately.
Dynamic scheduling is powered by a scheduler system that supports updating tasks on the fly. This lets you adjust timings or add new tasks without interrupting ongoing processes.
Persistence is achieved by storing all configuration updates locally in config.json. The API and scheduling components reference this file to determine current behavior.
The frontend provides a dashboard for remote configuration management, manual trigger of actions, and diary logging. This enables you to automate routines and track changes in a centralized interface.
A compact, reliable runtime is provided by the APScheduler-based system used for dynamic task management, ensuring you can reload scheduled jobs without downtime.
Stores all configuration changes locally in config.json to ensure durable settings across restarts.
Front-end loading attempts to synchronize the latest server configuration automatically on startup.
Uses APScheduler to reload or update tasks without restarting the server.