"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.

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

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.

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 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.

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.

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 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.

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.