"How to identify when runtime errors occur in a program" matching MCP servers:

Linux sandboxes for AI agents, each a Firecracker microVM. Run code, keep files, pause and wake with memory kept, snapshot and fork.

A high-performance MCP execution engine that runs tool code in isolated, resource-constrained sandboxes with context state persistence.

ai-runtime-guard is an MCP server that sits between your AI agent and your system, enforcing a policy layer before any file or shell action takes effect. No retraining, no prompt engineering, no changes to your agent or workflow, just install, configure once, and your agent operates within the boundaries you set.

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

Drop-in MCP server that lets Claude inspect, debug, and hot-fix your running Bun process through conversation.

Enables MCP-based communication with existing interactive terminal channels, such as shells or agent CLIs, by listing, reading, writing text, and sending control signals to tmux sessions without creating or managing them.

Enables ChatGPT Web conversations to operate a Windows computer through MCP with local tools, permissions, task state, and audit evidence.

Enables direct local development operations such as shell commands, interactive terminals, file patches, images, and execution history through MCP tools, accessible via HTTP/stdio or a ChatGPT tunnel.

Enables AI agents to control and interact with a local Windows computer through authenticated MCP tools, including file operations, shell commands, process management, HTTP requests, and system/media inspection.

Enables AI agents to securely connect to and manage remote servers via SSH, with tools for running commands, file operations, log inspection, service management, and system monitoring under configurable access controls and audit logging.

A local MCP runtime for macOS and Windows that enables ChatGPT to read, edit, search, run tests, and capture screenshots in your local project folder while keeping your source code on your machine.

PRIMS is a lightweight, open-source Model Context Protocol (MCP) server that lets LLM agents safely execute arbitrary Python code in a secure, throw-away sandbox.

Provides secure filesystem access for AI models through the Model Context Protocol with strict path validation, file operations, directory management, and system command execution within predefined directories.

A simple MCP server that exposes a terminal tool, allowing AI agents to execute shell commands.

The m2m-mcp-server-ssh-client provides a unified MCP server interface, securely connecting via SSH to the m2m-mcp-server-ssh-server back-end which aggregates MCP servers. This setup offers secure, single-point access to diverse remote tools using robust SSH key-based authentication.

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.

A self-hosted MCP server that enables AI coding agents to read, edit, search, and run code in local projects with human review loops and policy controls.

Enables MCP-compatible AI clients to connect to a Windows machine for file operations, PowerShell execution, Git inspection, task management, memory, and skills.

An MCP server that connects Claude Desktop to an interactive Claude Code session running in a tmux terminal.

A FastMCP server that enables AI agents to safely execute shell commands, run code in Python/Node.js/Go, and install packages, all packaged in Docker for secure deployment.