"Connecting a backend project to an Elasticsearch database" matching MCP servers:

Enables AI agents to debug and repair historical Solaris/SPARC systems by providing an out-of-band control plane with guest DTrace, QEMU monitor access, SPARC-aware GDB, host eBPF/perf tracing, and an immutable evidence ledger for cross-layer hypothesis testing.

Enables MCP clients to execute commands, edit files, run Git workflows, and perform validated independent reviews in real development environments through Docker-based runtimes with separated developer and auditor permissions.

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.

MCP server that enables AI models to open the Windows Notepad application via function calling, demonstrating the complete MCP tool call workflow.

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.

Convex’s MCP server lets you introspect tables, call functions, and read/write data seamlessly. Agents can generate one-off queries safely—thanks to Convex’s sandboxed queries, ensuring data integrity. Perfect for AI automation, real-time apps, and dynamic data access.

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.

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.

A tutorial MCP server for learning the Model Context Protocol by building file and system tools. Provides hands-on experience creating custom tools that enable AI models to interact with files and execute system commands.

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.

A security-first MCP server that provides LLMs with structured tools for filesystem, process, search, build/test/lint, IDE integration, and more.

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.

A single-purpose MCP server that lets an AI assistant run commands over SSH on a fixed, pre-configured host only, with an explicit per-call confirmation gate before execution.

Enables AI agents to securely use real secrets (API keys, database passwords) by requiring human approval for each release, ensuring secrets never enter the model's context.

MCP server for managing remote servers via SSH, enabling command execution, file transfer, rsync, tunnels, health checks, backups, and database operations.

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

An MCP server that lets AI assistants manage an OpenMediaVault NAS over SSH using OMV's own RPC interface, covering disks, filesystems, shares, users, S.M.A.R.T. health, services, and configuration changes. It exposes a small set of generic tools to discover and call RPC methods, plus optional shell access.

MCP Bridge is a local-first Linux/Windows desktop app connecting ChatGPT to approved files and Chrome through authorized OpenAI tunnels, with optional shell tools. The Glama-hosted v0.1.0 release is a restricted READ-ONLY documentation demo: five tools can access only bundled public examples, not your computer, browser, credentials, shell, or files.

A comprehensive Node.js server implementing Model Context Protocol (MCP) that enables filesystem operations, process management, and terminal session handling with an enterprise-grade security approach.

An MCP server that lets an AI agent use a real terminal the way a person does: type, press keys, click, read the screen, take a screenshot.

An MCP server that provides sandboxed shell command execution with configurable security policies, enabling safe AI-assisted command runs within a project repository.

Give AI agents a persistent, interactive terminal with support for SSH, REPLs, database CLIs, TUI apps, and long-running processes.

Runs shell commands inside an existing microsandbox sandbox rather than on the Windows host, tracking the working directory and translating paths between the host project folder and the sandbox mount. Exposes tools for foreground or background execution with timeouts, output retrieval, killing processes, and listing them.

Enables Claude to manage multiple SSH connections, execute remote commands, and transfer files across servers. Supports secure authentication, default directories, sudo operations, and deployment automation with profiles for different project types.

Enables an AI agent to control a genuinely visible Windows terminal window by spawning it, sending keystrokes, reading the rendered console buffer, and closing it so a human can watch every action in real time.

Enables AI assistants to execute shell commands safely within registered project roots, with blocking of dangerous operations, output filtering, and log inspection.