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127,264 tools. Last updated 2026-05-05 12:27

"namespace:io.github.capratesignals-cpu" matching MCP tools:

  • Executes a full SQL query clustering pipeline to extract, tokenize, embed, and cluster high-CPU queries, producing performance metrics and cluster statistics for optimization.
  • Analyze pre-computed SQL query cluster statistics to identify optimization opportunities. Rank clusters by CPU, I/O, skew, or complexity to pinpoint high-impact candidates and improve workload performance.
  • Get the number of available CPU cores to determine parallelism in agent workflows. Returns JSON with core count, or plain integer with --raw.
    MIT
  • Manage a security monitoring daemon on a server: install it to run checks on disk, RAM, CPU, and audit every 5 minutes; remove it; or check status for last check time and threshold breaches.
  • Fetch recent logs from Coolify, Docker, or system journal, or obtain CPU, RAM, and disk usage metrics from Kastell-managed servers via SSH.
  • List all Proxmox cluster nodes with their status, CPU load, memory usage, and uptime to quickly assess cluster health.
    MIT

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  • Check the current runtime status of a Proxmox VM, including whether it is running or stopped, CPU usage, memory usage, and uptime. Provide the node and VM ID to get real-time metrics.
    MIT
  • Retrieve the current runtime status of an LXC container, including running/stopped state, CPU, and memory usage, by specifying the node and container ID.
    MIT
  • Adjust CPU scheduling priority for a command to reduce CPU impact. Execute with niceness adjustment, capture output, and enforce a safety timeout.
    MIT
  • Retrieve historical performance metrics for a virtual machine, including CPU, memory, disk, network usage, and uptime. Ideal for monitoring VPS resource utilization over a specified timeframe.
    MIT
  • Retrieve detailed Redis server information and statistics. Specify a section such as memory or cpu to filter results.
    MIT
  • Retrieve detailed status of a Proxmox node including CPU usage, memory, disk, load average, and kernel version to monitor node health.
    MIT
  • Monitor and retrieve CPU usage data for AlibabaCloud ECS instances by specifying region and instance IDs, enabling efficient resource management and performance tracking.
    Apache 2.0
  • Monitor CPU load averages on AlibabaCloud ECS instances by retrieving one-minute load metrics, enabling performance tracking and resource optimization.
    Apache 2.0
  • Create a Sakura Cloud server with specified CPU, memory, and generation, then automatically provision a disk for it. Select from available server plans during setup.
    Apache 2.0
  • Retrieve CPU load average over 5 minutes for AlibabaCloud ECS instances. Monitor system performance and manage resource usage effectively with automated data collection.
    Apache 2.0