"Exploring RAG through defined workspaces" matching MCP servers:

Enables LLMs to manage Kasm Workspaces sessions, including creating, destroying, pausing, resuming, screenshotting, dispatching commands, and sharing view-only links, with opt-in administration of users, groups, and registries.

Enables coding agents to control and inspect a running Herdr terminal session, including listing workspaces, tabs, panes, and agents, reading pane output, prompting agents, sending keys and commands, waiting for output or state, and splitting or closing panes.

An all-in-one MCP server for local LLMs that provides live web access, math, shell/code execution, filesystem access, memory, and RAG over local files using LM Studio embeddings.

Enables AI clients to manage remote Linux workspaces over OpenSSH, including file read/write/edit, glob/grep search, shell command execution, and background job management, with authentication handled interactively in a terminal.

Terminal multiplexer MCP server for orchestrating parallel AI agents. Manages workspaces, panes, surfaces with send_input/read_screen/spawn_agent/stop_agent tools. Supports Claude Code, Codex, Gemini, Cursor CLI agents with lifecycle management, browser automation, and agent status push via Claude --channels.

Provides a secure, sandboxed interface for LLM agents to interact with HPC clusters via bubblewrap isolation, managing workspaces and executing commands locally or through Slurm.

Enables AI assistants to execute shell commands and manage long-running processes within persistent tmux sessions across isolated workspaces. It features a dual-window architecture to separate raw command execution from interactive terminal output.

MCP server to control Onda terminal from AI agents, providing tools for splitting panes, running commands, managing tabs and workspaces, and orchestrating multi-agent workflows across multiple windows.

Gives ChatGPT secure access to your local machine over MCP, letting it read and edit files, run commands, tests, and Git within approved workspaces.

Enables AI agents to manage and interact with Kasm Workspaces containerized desktop infrastructure, providing tools for session management, command execution, file operations, and user management via the Model Context Protocol.

Enables secure SSH-based operations on Oracle Linux 8+ hosts through a curated, YAML-defined capability catalog, with typed parameters, two-phase confirmations for mutations, and full audit logging.

Enables AI assistants to perform local development tasks on Windows by reading and editing files, running commands, and controlling browser and desktop tools, all within isolated workspaces. Supports secure remote access via an optional tunnel for ChatGPT clients.

MCP server enabling AI agents to interact with terminal applications through structured Terminal State Tree representation. Works with any AI assistant that supports the Model Context Protocol.

MCP server wrapping the cmux CLI to let agents control terminal panes, workspaces, input, and browser.

Enables ChatGPT to safely interact with Windows workspaces through a loopback-only MCP server, with OAuth, bounded PowerShell tools, persistent jobs, and optional Cloudflare Tunnel exposure.

A powerful tool for exploring, searching, and managing your shell command history through the MCP (Model Control Protocol) interface. This project allows you to easily access, search, and retrieve your previously executed shell commands.

Enables authenticated access to an SSH server fleet and RAG knowledge base through MCP tools, with read-only checks running automatically and risky operations requiring human approval. Supports target registry IDs, policy enforcement, credential management, and hybrid knowledge search.

Enables ChatGPT, Claude, and other web MCP clients to access and operate multiple VS Code workspaces through tools such as read, bash, edit, write, and workspace_info, with per-workspace tokens and role-based permissions.

Enables AI models to safely execute pre-defined Linux shell commands with a whitelist mechanism, restricting execution to allowed commands only.

Turns local project directories into persistent MCP workspaces, allowing AI agents to read files, modify code, run commands, manage Git, and save session progress across conversations.