"Understanding and Building Probability Density Functions (PDF) Models" matching MCP servers:

Enables a Claude Code cloud session to run shell commands inside a chroot jail on your own computer and to call the MCP servers already installed there, with every request authenticated and relayed over a Cloudflare quick tunnel so no router port is opened and no SSH key or tunnel is ever handed to the cloud VM.

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.

Enables AI models to run interactive tmux terminal sessions that a human can watch and type into, with all output logged and split into searchable per-command regions so only needed lines enter context. Also guides models through unfamiliar tools by returning exit statuses and key output alongside advice on the tool being used.

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.

An MCP server that enables large language models to connect to remote servers via SSH, execute commands, and transfer files securely.

A secure Model Context Protocol server that provides controlled filesystem access within predefined directories, enabling AI models to perform file and directory operations with strict path validation.

A Model Context Protocol server that allows language models to manage Hetzner Cloud resources through structured functions, including servers, volumes, firewalls, and SSH keys.

MCP server for Android device automation via ADB, enabling screen control, phone functions, app management, and device control.

A Model Context Protocol server that gives AI models access to your iTerm terminal session, allowing them to read output, write commands, and send control characters.

A server that enables secure interaction with remote SSH hosts through standardized MCP interface, providing functions like listing hosts, executing commands, and transferring files using native SSH tools.

An MCP server that allows AI models to execute system commands on local machines or remote hosts via SSH, supporting persistent sessions and environment variables.

A secure server for executing terminal commands within predefined paths, allowing safe interaction by Large Language Models with operating system environments.

Enables AI models to access and manage SSH connections through a standardized interface, allowing AI assistants to perform SSH operations like connecting to servers, executing commands, and managing files.

MCP server for running shell commands on a Linux development board via UART serial.

Bridges large language models with the Cobalt Strike C2 framework, enabling AI assistants to control adversary simulation workflows through natural language.

A remote MCP bridge that lets MCP clients use Modal sandboxes for Linux execution, dependency installation, Git operations, GPU jobs, and invoking deployed Modal Functions.

A comprehensive MCP server that enables file operations, mathematical calculations with unit conversions, and system information retrieval. Provides secure access to local file system, calculator functions with statistics, and system monitoring capabilities.

A Cloudflare Workers-based deployment that implements the Model Context Protocol, allowing AI models to use custom tools without authentication.

Provides a bridge between large language models and the Metasploit Framework, enabling AI assistants to access and control penetration testing functionality through natural language.

Simple MCP Runner makes it effortless to safely expose system commands to language models via a lightweight MCP server—all configurable with a clean, minimal YAML file and zero boilerplate.

Enables AI agents to assess the risk of shell commands before execution, providing a probability of harm and a suggested action (auto, warn, or confirm).

Enables AI agents to access the local machine over MCP for terminal commands, file operations, code search, process management, and reading or editing PDF, Excel, and Word documents, with no telemetry.

Parselbox is a local, open-source execution layer that turns MCP servers, APIs, shells and host functions into native Python objects. Agents discover capabilities on demand and compose them with code, files, packages and background tasks in one stateful workspace. Powered by Deno and Pyodide.

MCP server for building and driving VMware Workstation VMs from an AI agent, enabling unattended OS installs, guest command execution, file transfer, screenshots, and fleet management.

Enables AI assistants to execute Python functions and system commands via Streamable HTTP, including bash, Python code execution, file operations, and text searching.

Enables AI agents to monitor system resources and control media, brightness, volume, and other Windows PC functions through natural language commands.

Bridges Llama models with Claude Desktop through Ollama, enabling privacy-first local AI operations with 10+ built-in tools for file operations, web search, calculations, and custom model deployment. Features streaming support, hybrid intelligence workflows, and extensive Python ecosystem integration for research, development, and enterprise applications.

Provides advanced web scraping with HTTP client, smart content extraction to Markdown, browser automation via Playwright, screenshot/PDF generation, and Docker sandbox execution environments.

Enables AI clients to securely operate servers over SSH, including remote command execution and file transfer with a built-in web admin console and Windows tray app.

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