"Local RAG system for providing documentation to a large language model (LLM)" matching MCP servers:

Enables monitoring of Ubuntu system metrics, Docker containers, and public IP drift, with Feishu alerting and HTTP/MCP tools for querying status, viewing history, and restarting containers.

Enables Claude to monitor CPU, memory, disk, processes, network connections, Docker containers, and system logs for system diagnostics and troubleshooting.

Provides system hardware information (CPU, memory, storage, etc.) and system status (OS, network, resource usage) via MCP tools.

Provides real-time system metrics and information through a Model Context Protocol interface, enabling access to CPU usage, memory statistics, disk information, network status, and running processes.

Enables AI clients to access real-time system information such as memory and swap usage from a Linux machine via MCP tools.

Read-only MCP server for observing Linux hosts (systemd, ZFS, Docker, VMs, network, hardware, NixOS) as a graph of objects with provenance. Provides seven tools to list hosts, get status, collections, objects, evidence, lookup, and changes.

Enables comprehensive system monitoring and diagnostics through 18 tools that provide detailed information about CPU, memory, disk usage, network interfaces, running processes, battery status, hardware details, and temperature monitoring. Allows users to query system information and performance metrics through natural language interactions.

MCP server that coordinates autonomous infrastructure health monitoring agents, enabling natural-language telemetry queries and automated diagnosis, remediation, and verification.

A secure, local-first MCP server for read-only inspection and troubleshooting of development environments, exposing narrow, typed, auditable capabilities for repository inspection, log summarization, Docker review, and security scanning without granting unrestricted machine access.

Enables MCP-aware AI agents to query the local machine's health over stdio, retrieving CPU, memory, disk, and load-average metrics along with boot time and the top processes by CPU usage. It gives coding agents a structured, protocol-standard way to answer questions like whether it is safe to run a build or what is currently consuming resources.

Enables AI assistants to interact with local Docker containers, Kubernetes clusters, and system logs using the Model Context Protocol.

Provides a read-only summary of Ubuntu server status including system version, resource usage, apt updates, service health, security logs, and hardware information to AI clients via MCP tools.

A modular Retrieval-Augmented Generation (RAG) framework that provides hybrid search and knowledge retrieval capabilities via the Model Context Protocol. It enables users to integrate document-based knowledge into LLM workflows with support for dense/sparse retrieval, reranking, and observability.

Enables LLMs to monitor system health, manage processes, execute shell commands, and inspect environment variables and directories.

MCP server for tracking LLM costs, setting spending limits, and receiving budget alerts directly within AI editors via slash commands like /guard.

Provides AI agents with tools to retrieve live system statistics (CPU, memory) and safely evaluate basic arithmetic expressions.

Provides real-time Linux system monitoring for CPU load, memory usage, disk space, and process activity. This server enables users to retrieve comprehensive performance metrics and resource utilization data through a standardized interface.

Enables read-only querying of a local Grafana Loki instance, providing log search, service listing, and health check capabilities.

Gives AI agents real-time access to system metrics, process management, and container orchestration.

Enables LLMs to interact with Kibana through composable toolboxes for building dashboards, managing data, and handling alerts, among other operations.