"Context Memory Systems and Techniques" matching MCP servers:

Enables AI agents to manage system updates, application installations, and remote host orchestration through MCP tools.

MCP server for memory management with process inspection and cleanup tools.

Enables AI clients to securely execute remote commands and transfer files over SSH through standardized MCP tools, with multi-host management, flexible authentication, and an admin console.

Memory bank with Server as SSH support for central knowledge base

A lightweight service that enables AI assistants to execute AWS CLI commands through the Model Context Protocol (MCP), allowing AI tools to retrieve AWS documentation and interact with AWS services.

Connects AI assistants to a Kali Linux terminal for executing security tools, managing interactive shell sessions, and performing penetration testing, CTF challenges, and security research tasks.

An MCP server for integrating AI models with client applications, allowing for structured interactions with APIs using command-line arguments for authentication and configuration.

A comprehensive production-ready MCP server with AI integration, plugin management, and web-based administration. Features multi-database support, RAG capabilities, SSH/SFTP access, and a built-in plugin hub for managing the MCP ecosystem.

An MCP server for driving an ITS session on a PiDP-10 emulator over raw TCP, supporting persistent connections, raw control-byte transmission, and escape syntax for DDT commands. It enables interaction with ITS systems through natural language via MCP tools like open, send, read, and status.

A Model Context Protocol server that allows LLMs to securely execute shell commands on remote Linux and Windows systems via SSH. It supports password and key-based authentication, command timeouts, and sudo elevation for administrative tasks.

Enables secure command-line interactions on Windows systems with support for PowerShell, CMD, Git Bash, and WSL shells, providing controlled file access, command execution, and configurable security restrictions.

A local Model Context Protocol server that allows LLMs to securely execute shell commands on remote Linux and Windows systems via SSH connections.

A locally-running Python REPL server that integrates with Claude Desktop through the Model Context Protocol, offering shell bridge, streaming, and operational memory features for secure code experimentation and system automation.

Enables read-only Linux system diagnostics and troubleshooting on local and remote RHEL-based systems via SSH, including services, processes, logs, network, and storage analysis.

Enables read-only Linux system diagnostics and troubleshooting on RHEL-based systems, including system info, services, processes, logs, network, and storage analysis. Supports both local and remote SSH execution across multiple hosts.

SSH Session MCP fills a gap in the MCP ecosystem by offering a persistent shared SSH PTY runtime, not just stateless command execution. It features browser collaboration, input locking, safe/full execution modes, async command tracking, configurable policy rules, and multi-device profiles. Ideal for remote development, embedded systems, infrastructure workflows, and hardware control scenarios.

A comprehensive Model Context Protocol (MCP) server that enables Claude and other LLM applications to execute PowerShell commands, scripts, and perform system operations on Windows systems.

A comprehensive Model Context Protocol server that enables AI assistants to interact with and manage Windows systems, providing capabilities for file system operations, process management, system information retrieval, registry operations, service management, network diagnostics, and performance monitoring.

An MCP server for read-only Linux system administration and diagnostics on RHEL-based systems via SSH. It enables users to troubleshoot remote hosts by accessing system information, services, logs, and network configurations through natural language.

About AI/ML-powered diagnostic engine for SRE Observability on Konflux and OpenShift. It uses the Model Context Protocol (MCP) and 40+ tools to analyze logs, metrics, and traces, enabling automated RCA and predictive analysis.