Model Context Protocol (MCP) server for scheduling and managing tasks through a standardized API. The server provides task scheduling capabilities supporting both shell commands and AI-powered tasks, all accessible via the MCP protocol.

• Schedule shell command or prompt to AI tasks using cron expressions
• AI can have access to MCP servers
• Manage tasks via MCP protocol
• Task execution with command output capture
• Task persistence across restarts (SQLite)
• Multi-instance safe — multiple instances can share the same database without duplicate execution
• Support multiple isolated instances with different --db-path

npx -y mcp-cron

claude mcp add mcp-cron -- npx -y mcp-cron

{
  "mcpServers": {
    "mcp-cron": {
      "command": "npx",
      "args": ["-y", "mcp-cron", "--transport", "stdio"]
    }
  }
}

A more complete setup with AI provider, model selection, and sleep prevention:

{
  "mcpServers": {
    "mcp-cron": {
      "command": "npx",
      "args": [
        "-y", "mcp-cron",
        "--transport", "stdio",
        "--prevent-sleep",
        "--ai-provider", "anthropic",
        "--ai-model", "claude-sonnet-4-5-20250929"
      ],
      "env": {
        "ANTHROPIC_API_KEY": "your-api-key"
      }
    }
  }
}

To route AI tasks through a LiteLLM proxy:

{
  "mcpServers": {
    "mcp-cron": {
      "command": "npx",
      "args": [
        "-y", "mcp-cron",
        "--transport", "stdio",
        "--prevent-sleep",
        "--ai-base-url", "https://litellm.yourcompany.com",
        "--ai-model", "claude-sonnet-4-5-20250929"
      ],
      "env": {
        "MCP_CRON_AI_API_KEY": "sk-your-litellm-key"
      }
    }
  }
}

The --ai-model value should match a model name in your LiteLLM proxy config. LiteLLM exposes an OpenAI-compatible API, so --ai-provider can be omitted (defaults to openai). When a custom base URL is set, mcp-cron automatically uses the Chat Completions API instead of the Responses API. The Responses API is only used for direct OpenAI (api.openai.com) and Azure OpenAI (*.openai.azure.com) endpoints.

See Command Line Arguments and Environment Variables for all available options.

• Go 1.24.0 or higher

# Clone the repository
git clone https://github.com/jolks/mcp-cron.git
cd mcp-cron

# Build the application as mcp-cron binary
go build -o mcp-cron cmd/mcp-cron/main.go

The server supports two transport modes:

• HTTP (Streamable HTTP): Default HTTP-based transport for browser and network clients
• stdio: Standard input/output transport for direct piping and inter-process communication

# Start the server with Streamable HTTP transport (default mode)
# Default to localhost:8080
./mcp-cron

# Start with custom address and port
./mcp-cron --address 127.0.0.1 --port 9090

Config file example

{
  "mcpServers": {
    "mcp-cron": {
      "url": "http://localhost:8080"
    }
  }
}

The stdio transport is particularly useful for:

• Direct piping to/from other processes
• Integration with CLI tools
• Testing in environments without HTTP
• Docker container integration

Upon starting Cursor IDE and Claude Desktop, it will automatically start the server

Config file example

{
  "mcpServers": {
    "mcp-cron": {
      "command": "<path to where mcp-cron binary is located>/mcp-cron",
      "args": ["--transport", "stdio"]
    }
  }
}

The following command line arguments are supported:

The following environment variables are supported:

On laptops, the system may go to sleep and prevent scheduled tasks from running on time. Use the --prevent-sleep flag to keep the system awake while mcp-cron is running:

mcp-cron --prevent-sleep --transport stdio

Or via environment variable:

MCP_CRON_PREVENT_SLEEP=true mcp-cron --transport stdio

Note: This prevents idle sleep only. It does not prevent sleep from closing the laptop lid or pressing the power button.

When running with the default HTTP transport, logs are output to the console.

When running with stdio transport, logs are redirected to a mcp-cron.log log file to prevent interference with the JSON-RPC protocol:

• Log file location: Same location as mcp-cron binary.
• Task outputs, execution details, and server diagnostics are written to this file.
• The stdout/stderr streams are kept clean for protocol messages only.

The server exposes several tools through the MCP protocol:

1. list_tasks - Lists all tasks (scheduled and on-demand)
2. get_task - Gets a specific task by ID
3. add_task - Adds a new shell command task (provide schedule for recurring, or omit for on-demand)
4. add_ai_task - Adds a new AI (LLM) task with a prompt (provide schedule for recurring, or omit for on-demand)
5. update_task - Updates an existing task
6. remove_task - Removes a task by ID
7. run_task - Executes a task by ID, waits for completion, and returns the result (for on-demand tasks or ad-hoc runs of scheduled tasks)
8. enable_task - Enables a task so it runs on its schedule or can be triggered via run_task
9. disable_task - Disables a task so it stops running and cannot be triggered
10. get_task_result - Gets execution results for a task (latest by default, or recent history with limit)
11. query_task_result - Runs a read-only SQL query against the database (SELECT only, capped at 1000 rows)

Tasks have the following structure:

{
  "id": "task_a3f7b2c9e1d04f68",
  "name": "Example Task",
  "schedule": "0 */5 * * * *",
  "command": "echo 'Task executed!'",
  "prompt": "Analyze yesterday's sales data and provide a summary",
  "type": "shell_command",
  "description": "An example task that runs every 5 minutes",
  "enabled": true,
  "lastRun": "2025-01-01T12:00:00Z",
  "nextRun": "2025-01-01T12:05:00Z",
  "status": "completed",
  "createdAt": "2025-01-01T00:00:00Z",
  "updatedAt": "2025-01-01T12:00:00Z"
}

For shell command tasks, use the command field to specify the command to execute. For AI tasks, use the prompt field to specify what the AI should do. The type field can be either shell_command (default) or AI.

Scheduled vs on-demand tasks:

• Scheduled: Provide a schedule (cron expression) — the task runs automatically on that schedule.
• On-demand: Omit schedule — the task sits idle until triggered via run_task.

run_task also works on scheduled tasks for ad-hoc execution outside their normal schedule. After execution, scheduled tasks resume their normal schedule; on-demand tasks return to idle.

The tasks can have the following status values:

• pending - Task has not been run yet
• running - Task is currently running
• completed - Task has successfully completed
• failed - Task has failed during execution
• disabled - Task is disabled and won't run on schedule

Cron expressions are required for scheduled tasks and omitted for on-demand tasks. The scheduler uses the github.com/robfig/cron/v3 library for parsing. The format includes seconds:

┌───────────── second (0 - 59) (Optional)
│ ┌───────────── minute (0 - 59)
│ │ ┌───────────── hour (0 - 23)
│ │ │ ┌───────────── day of the month (1 - 31)
│ │ │ │ ┌───────────── month (1 - 12)
│ │ │ │ │ ┌───────────── day of the week (0 - 6) (Sunday to Saturday)
│ │ │ │ │ │
│ │ │ │ │ │
* * * * * *

Examples:

• 0 */5 * * * * - Every 5 minutes (at 0 seconds)
• 0 0 * * * * - Every hour
• 0 0 0 * * * - Every day at midnight
• 0 0 12 * * MON-FRI - Every weekday at noon

go build -o mcp-cron cmd/mcp-cron/main.go

See docs/testing.md for the full testing guide, including integration tests and AI task tests.

• modelcontextprotocol/go-sdk - Official Go SDK for the Model Context Protocol
• robfig/cron - Cron expression parsing for Go