Cronlytic MCP Server

What does “Cronlytic” MCP Server do?

The Cronlytic MCP Server is a comprehensive Model Context Protocol (MCP) server designed to integrate with the Cronlytic API, enabling AI agents and LLM-powered applications to interact seamlessly with serverless cron job infrastructure. It provides a bridge for large language models to perform CRUD operations—create, read, update, and delete—on cron jobs, as well as advanced tasks such as pausing, resuming, and monitoring job execution. The server also supports log access and performance metrics retrieval, enhancing observability and automation. By exposing these features through standardized MCP interfaces, Cronlytic MCP Server streamlines workflow automation for developers, allowing direct interaction with Cronlytic resources via natural language or programmatic requests within AI development environments.

List of Prompts

• 18 comprehensive prompts are available for guided assistance and workflow standardization (specific prompt templates are referenced, but not listed in the repository).

List of Resources

• Dynamic job resources: Enables LLMs to access and manage cron job definitions and metadata.
• Cron templates: Provides reusable scheduling templates to accelerate job setup.
• Execution logs: Exposes logs for job runs, aiding in debugging and monitoring.
• Performance metrics: Makes job performance and system health data accessible for analysis.

List of Tools

• Health Check: Verifies server and API connectivity.
• Create Job: Registers a new cron job.
• Read Job: Retrieves job details.
• Update Job: Modifies existing cron job parameters.
• Delete Job: Removes a cron job.
• Pause Job: Temporarily halts job execution.
• Resume Job: Restarts job execution for a paused job.
• List Jobs: Enumerates all jobs for the account.
• Get Logs: Fetches execution logs for a specific job.

Use Cases of this MCP Server

• Automated Cron Job Management: Developers can create, modify, and delete serverless cron jobs directly from their LLM-powered tools, reducing manual dashboard use.
• Monitoring and Troubleshooting: LLMs can retrieve execution logs and performance metrics, facilitating rapid debugging and system health checks.
• Bulk Operations on Jobs: Use AI agents to list, pause, or resume multiple jobs programmatically, streamlining maintenance and operations.
• Template-Driven Scheduling: Leverage cron templates as reusable resources, accelerating onboarding and consistency across teams.
• Security and Audit Automation: Integrate with automated flows to periodically audit job configurations and detect anomalies.

How to set it up

Windsurf

1. Ensure Python 3.8+ is installed and Cronlytic MCP Server is running.
2. Open Windsurf configuration (e.g., ~/.windsurf/config.json).
3. Add the Cronlytic MCP Server:

   {
     "mcpServers": {
       "cronlytic": {
         "command": "python",
         "args": ["-m", "cronlytic_mcp_server"]
       }
     }
   }
   

4. Save and restart Windsurf.
5. Verify that the server is accessible.

Claude

1. Make sure Python 3.8+ is installed and Cronlytic MCP Server is operational.
2. Open Claude’s integration config (e.g., ~/.claude/config.json).
3. Insert the following:

   {
     "mcpServers": {
       "cronlytic": {
         "command": "python",
         "args": ["-m", "cronlytic_mcp_server"]
       }
     }
   }
   

4. Save and restart Claude.
5. Confirm integration by running a health check prompt.

Cursor

1. Confirm Python 3.8+ and Cronlytic MCP Server availability.
2. Edit Cursor config (e.g., ~/.cursor/config.json).
3. Add:

   {
     "mcpServers": {
       "cronlytic": {
         "command": "python",
         "args": ["-m", "cronlytic_mcp_server"]
       }
     }
   }
   

4. Save and relaunch Cursor.
5. Test with a simple job listing prompt.

Cline

1. Validate Python 3.8+ and Cronlytic MCP Server setup.
2. Locate Cline config (e.g., ~/.cline/config.json).
3. Insert:

   {
     "mcpServers": {
       "cronlytic": {
         "command": "python",
         "args": ["-m", "cronlytic_mcp_server"]
       }
     }
   }
   

4. Save and restart Cline.
5. Run a job status check to verify.

Securing API Keys with Environment Variables Store API keys and user IDs securely:

{
  "env": {
    "CRONLYTIC_API_KEY": "your_api_key_here",
    "CRONLYTIC_USER_ID": "your_user_id_here"
  },
  "inputs": {
    "base_url": "https://api.cronlytic.com/prog"
  }
}

Alternatively, use a configuration file as described in the repository.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "cronlytic": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent can use this MCP as a tool with access to all its functions and capabilities. Be sure to replace "cronlytic" and the URL with your actual MCP server name and address.

Overview

Our opinion

The Cronlytic MCP Server is well-documented and production-ready, with comprehensive functionality for serverless cron job management. It covers all major MCP features except for explicit sampling and roots support. Prompt and tool coverage is very strong, but the open-source community engagement is low.

MCP Score

Rating:
Based on the tables above, I would rate this MCP server a 7/10: it is production-ready, feature-rich, and well-documented, but lacks sampling/roots info, open-source traction, and an explicit license.