scheduler-mcp

A robust task scheduler server built with Model Context Protocol (MCP) for scheduling and managing various types of automated tasks.

MCP Scheduler is a versatile task automation system that allows you to schedule and run different types of tasks:

• Shell Commands: Execute system commands on a schedule
• API Calls: Make HTTP requests to external services
• AI Tasks: Generate content through OpenAI models
• Reminders: Display desktop notifications with sound

The scheduler uses cron expressions for flexible timing and provides a complete history of task executions. It's built on the Model Context Protocol (MCP), making it easy to integrate with AI assistants and other MCP-compatible clients.

• Multiple Task Types: Support for shell commands, API calls, AI content generation, and desktop notifications
• Cron Scheduling: Familiar cron syntax for precise scheduling control
• Run Once or Recurring: Option to run tasks just once or repeatedly on schedule
• Execution History: Track successful and failed task executions
• Cross-Platform: Works on Windows, macOS, and Linux
• Interactive Notifications: Desktop alerts with sound for reminder tasks
• MCP Integration: Seamless connection with AI assistants and tools
• Robust Error Handling: Comprehensive logging and error recovery

• Python 3.10 or higher
• uv (recommended package manager)

# For Mac/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# For Windows (PowerShell)
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

After installing uv, restart your terminal to ensure the command is available.

# Clone the repository
git clone https://github.com/yourusername/mcp-scheduler.git
cd mcp-scheduler

# Create and activate a virtual environment with uv
uv venv
source .venv/bin/activate  # On Unix/MacOS
# or
.venv\Scripts\activate     # On Windows

# Install dependencies with uv
uv pip install -r requirements.txt

If you prefer using standard pip:

# Clone the repository
git clone https://github.com/yourusername/mcp-scheduler.git
cd mcp-scheduler

# Create and activate a virtual environment
python -m venv .venv
source .venv/bin/activate  # On Unix/MacOS
# or
.venv\Scripts\activate     # On Windows

# Install dependencies
pip install -r requirements.txt

# Run with default settings (stdio transport)
python main.py

# Run with server transport on specific port
python main.py --transport sse --port 8080

# Run with debug mode for detailed logging
python main.py --debug

To use your MCP Scheduler with Claude Desktop:

1. Make sure you have Claude Desktop installed
2. Open your Claude Desktop App configuration at:
   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %APPDATA%\Claude\claude_desktop_config.json
3. Create the file if it doesn't exist, and add your server:

{
  "mcpServers": [
    {
      "type": "stdio",
      "name": "MCP Scheduler",
      "command": "python",
      "args": ["/path/to/your/mcp-scheduler/main.py"]
    }
  ]
}

Alternatively, use the fastmcp utility if you're using the FastMCP library:

# Install your server in Claude Desktop
fastmcp install main.py --name "Task Scheduler"

--address        Server address (default: localhost)
--port           Server port (default: 8080)
--transport      Transport mode (sse or stdio) (default: stdio)
--log-level      Logging level (default: INFO)
--log-file       Log file path (default: mcp_scheduler.log)
--db-path        SQLite database path (default: scheduler.db)
--config         Path to JSON configuration file
--ai-model       AI model to use for AI tasks (default: gpt-4o)
--version        Show version and exit
--debug          Enable debug mode with full traceback
--fix-json       Enable JSON fixing for malformed messages

You can use a JSON configuration file instead of command-line arguments:

{
  "server": {
    "name": "mcp-scheduler",
    "version": "0.1.0",
    "address": "localhost",
    "port": 8080,
    "transport": "sse"
  },
  "database": {
    "path": "scheduler.db"
  },
  "logging": {
    "level": "INFO",
    "file": "mcp_scheduler.log"
  },
  "scheduler": {
    "check_interval": 5,
    "execution_timeout": 300
  },
  "ai": {
    "model": "gpt-4o",
    "openai_api_key": "your-api-key"
  }
}

The MCP Scheduler provides the following tools:

• list_tasks: Get all scheduled tasks
• get_task: Get details of a specific task
• add_command_task: Add a new shell command task
• add_api_task: Add a new API call task
• add_ai_task: Add a new AI task
• add_reminder_task: Add a new reminder task with desktop notification
• update_task: Update an existing task
• remove_task: Delete a task
• enable_task: Enable a disabled task
• disable_task: Disable an active task
• run_task_now: Run a task immediately

• get_task_executions: Get execution history for a task
• get_server_info: Get server information

MCP Scheduler uses standard cron expressions for scheduling. Here are some examples:

• 0 0 * * * - Daily at midnight
• 0 */2 * * * - Every 2 hours
• 0 9-17 * * 1-5 - Every hour from 9 AM to 5 PM, Monday to Friday
• 0 0 1 * * - At midnight on the first day of each month
• 0 0 * * 0 - At midnight every Sunday

The scheduler can be configured using environment variables:

• MCP_SCHEDULER_NAME: Server name (default: mcp-scheduler)
• MCP_SCHEDULER_VERSION: Server version (default: 0.1.0)
• MCP_SCHEDULER_ADDRESS: Server address (default: localhost)
• MCP_SCHEDULER_PORT: Server port (default: 8080)
• MCP_SCHEDULER_TRANSPORT: Transport mode (default: stdio)
• MCP_SCHEDULER_LOG_LEVEL: Logging level (default: INFO)
• MCP_SCHEDULER_LOG_FILE: Log file path
• MCP_SCHEDULER_DB_PATH: Database path (default: scheduler.db)
• MCP_SCHEDULER_CHECK_INTERVAL: How often to check for tasks (default: 5 seconds)
• MCP_SCHEDULER_EXECUTION_TIMEOUT: Task execution timeout (default: 300 seconds)
• MCP_SCHEDULER_AI_MODEL: OpenAI model for AI tasks (default: gpt-4o)
• OPENAI_API_KEY: API key for OpenAI tasks

await scheduler.add_command_task(
    name="Backup Database",
    schedule="0 0 * * *",  # Midnight every day
    command="pg_dump -U postgres mydb > /backups/mydb_$(date +%Y%m%d).sql",
    description="Daily database backup",
    do_only_once=False  # Recurring task
)

await scheduler.add_api_task(
    name="Fetch Weather Data",
    schedule="0 */6 * * *",  # Every 6 hours
    api_url="https://api.weather.gov/stations/KJFK/observations/latest",
    api_method="GET",
    description="Get latest weather observations",
    do_only_once=False
)

await scheduler.add_ai_task(
    name="Generate Weekly Report",
    schedule="0 9 * * 1",  # 9 AM every Monday
    prompt="Generate a summary of the previous week's sales data.",
    description="Weekly sales report generation",
    do_only_once=False
)

await scheduler.add_reminder_task(
    name="Team Meeting",
    schedule="30 9 * * 2,4",  # 9:30 AM every Tuesday and Thursday
    message="Don't forget the team standup meeting!",
    title="Meeting Reminder",
    do_only_once=False
)

If you want to contribute or develop the MCP Scheduler further, here are some additional commands:

# Install the MCP SDK for development
uv pip install "mcp[cli]>=1.4.0"

# Or for FastMCP (alternative implementation)
uv pip install fastmcp

# Testing your MCP server
# With the MCP Inspector tool
mcp inspect --stdio -- python main.py

# Or with a simple MCP client
python -m mcp.client.stdio python main.py

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

This project is licensed under the MIT License - see the LICENSE file for details.

• Built on the Model Context Protocol
• Uses croniter for cron parsing
• Uses OpenAI API for AI tasks
• Uses FastMCP for enhanced MCP functionality