"Debugging WebRTC SDK Implementation Issues in Visual Studio 2019" matching MCP servers:

Gives a Microsoft Copilot Studio agent Claude-Code-style tools to read, edit, search, and run shell commands against your local filesystem.

A Python implementation of the Model Context Protocol that allows applications to provide standardized context for LLMs, enabling creation of servers that expose data and functionality to LLM applications through resources, tools, and prompts.

Enables a Copilot Studio agent to operate a dedicated, disposable Windows VM by running commands, PowerShell scripts, file operations, and background jobs over authenticated HTTPS.

An MCP server that enables AI assistants to execute shell commands and manage files on remote servers via SSH. It supports recursive directory uploads, archive extraction, and direct file creation from text content.

Provides local agent capabilities for LM Studio including file I/O, terminal execution, and web search.

Enables LLMs to perform live debugging, dump analysis, and remote debugging by exposing Microsoft CDB capabilities through MCP. It manages cdb.exe sessions, forwards commands, and returns raw debugger output.

Enables AI assistants to manage remote servers via SSH with 14 commands for execution, file transfer, auditing, and monitoring.

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.

An MCP server that gives AI agents smart hands for physical machines, controlling them via IP-KVM, BMC, and SSH with visual boot-phase classification and operator-gated actions.

Enables secure remote command execution and file operations over SSH/SFTP with automatic authentication resolution. Features built-in diagnostics that identify connection issues—such as stale host keys, unloaded agents, or permission errors—and provide specific commands to fix them.

Shell and file edit capabilities tightly coupled to provide a powerful coding experience. You can work with different modes: architect and code-writer for separate plan and implementation phase. You can get it to invoke any cli command, e.g., compiling, type checking, linting, github cli, python

Enables programmatic control over tmux terminal sessions for SSH access, command execution, and terminal automation. Supports creating sessions, sending commands, capturing output, and managing multiple panes for interactive debugging and monitoring.

ChannelTerm is an open-source MCP server that enables humans and AI agents to share live serial and SSH terminal sessions for embedded systems development and debugging. It allows AI agents to read device output, execute commands, assist with GDB debugging, and transfer files while keeping developers in control.

Enables crash dump analysis and kernel debugging by integrating the real crash utility with automatic dump discovery, kernel matching, and session management.

An MCP server that enables users to execute arbitrary shell commands on their local machine and receive the output. It provides a terminal tool for running system commands through MCP-compatible clients using the Python SDK.

A secure server that enables AI applications to execute shell commands in specified directories, supporting multiple shell types (bash, sh, cmd, powershell) with built-in security features like directory isolation and timeout control.

Your sysadmin co-pilot — an AI that administers Linux through typed, approval-gated, Ed25519-audited actions instead of shell strings. Reference implementation of the LACS standard.

Enables an LLM agent to control a Windows guest in VMware Workstation, including power and snapshot management, kernel debugging via KDNET or serial, real-time terminal access, file transfer, and console screenshots, with synchronized state across tools.

A Node.js implementation of the Model Context Protocol that provides secure shell command execution capabilities, allowing AI models like Claude to run shell commands in a controlled environment with built-in security measures.

An MCP server that exposes pwndbg commands running under LLDB as tools for AI assistants. This enables AI-driven binary analysis, exploit development, and reverse engineering through pwndbg's enhanced debugging capabilities.

reference MCP server over STDIO — every SDK feature demonstrated

A distributable Model Context Protocol (MCP) server that exposes Dart SDK commands for AI-powered development. This server bridges the gap between AI coding assistants and Dart/Flutter development workflows by implementing the Model Context Protocol (MCP).

An MCP server that enables AI agents to execute terminal commands and Python code on the host system, leveraging Agent Zero's battle-tested implementation with session management and smart output handling.

Enables Claude to execute PowerShell commands and scripts in a persistent interactive terminal on Windows, with real-time output streaming to a web terminal.

Exposes a governed, OAuth-gated suite of 38 tools that let web AI clients work directly on your machine: reading, writing, and editing files, running whitelisted shell commands, searching, Git, SQLite, browser automation, DevTools debugging, background tasks, localhost/LAN fetches, clipboard, notifications, port management, and Postman control.

A desktop launcher and local MCP workspace server that enables ChatGPT and other MCP clients to securely read, edit, search, run commands, and show changes in selected local project folders.

Disposable Firecracker microVMs as sandboxes for AI agents: run commands and move files in any OCI image, snapshot and fork running copies in ~150 ms, egress allowlists. Self-hosted; needs Linux with KVM.

Enables interaction with a persistent PowerShell session through LM Studio. Provides tools to run commands, manage directories, and handle environment variables while maintaining session state between calls.

Enables AI agents to spawn and interact with real terminal sessions, capturing screenshots of rendered TUI output and sharing live sessions for debugging.

Enables AI agents to execute commands in a shared tmux session that is simultaneously visible to a human via a web-based terminal UI.