"Personalized Summary Service" matching MCP servers:

An MCP (Multi-Agent Conversation Protocol) server that enables interaction with Google's Managed Service for Microsoft Active Directory through its OpenAPI, allowing users to manage identity resources through natural language.

Enables MCP-compatible AI clients to securely monitor and manage a self-hosted Linux VPS over SSH, including service status, disk and RAM usage, TLS certificate expiry, fail2ban summaries, and two-step confirmed service restarts or IP unbans.

Provides agents with real Linux VMs to run commands, manage files, expose ports, and track costs, all through an ephemeral sandbox service.

Enables AI assistants to manage remote servers via SSH with agentless command execution, file operations, and service management.

An MCP server that reports on and manages systemd services using systemctl and journalctl, enabling service listing, status, logs, and control operations.

Enables MCP-capable agents to discover or connect to a phone-hosted MCP service and forward SSH command execution, session, and sudo command tools over stdio.

Enables local management of Raspberry Pi devices over SSH from VS Code, including status checks, remote file reads, systemd service inspection and control, command execution, and power actions with explicit opt-in security safeguards.

Local multi-package manager cache cleaning MCP service that safely discovers, previews, and cleans caches for Maven, pnpm, npm, yarn, pip, uv, and Go.

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.

Visual network topology editor with AI agent integration via MCP. One-click SSH + web-service access from any node, nmap/CSV import, smart auto-layout, multi-sheet, local-first. Open-source successor to netViz (CA Technologies 1990-2012)

ssh-mcp-pro is a secure Model Context Protocol (MCP) server for SSH automation, enabling clients to open SSH sessions, run commands, manage files, transfer artifacts, create tunnels, and perform package/service operations under policy control.

Enables system automation and control including command execution, process management, network tools, environment variables, disk usage, and service status.

A structured SSH management service that exposes command execution, file transfer, background jobs, GPU monitoring, systemd operations, and port forwarding as MCP tools with strict security and explicit input schemas.

Enables AI agents to securely manage and execute commands on remote Windows servers via WinRM, including PowerShell execution, system information retrieval, and service management.

Connects coding agents to local development services managed by Kranz. Agents can inspect service health, ports, logs, and run history, and perform lifecycle actions through the same live runtime as the TUI and CLI.

Provides a secure MCP interface to execute commands inside Docker Compose service containers, with real-time output capture and configurable timeouts.

The Yeepay MCP service provides integration with Yeepay services via the Model Context Protocol (MCP).

Servagent is an MCP server that enables a remote AI to take full control of a Linux server: command execution, file management, service administration, and more.

Enables AI agents to fetch Nebius cloud service documentation and execute Nebius CLI commands for managing cloud resources like compute instances and storage buckets through natural language interactions.

Talk to AWS CLI from an MCP-speaking agent. Run any aws <service> <operation> command through the Model Context Protocol, with read-only defaults and gated write access.

AI-native oscilloscope control. A pure-Python MCP service that lets LLM agents control Keysight EDUX1052G oscilloscopes via a compact DSL.

A command-line interface and MCP server for managing and monitoring multiple development services through a unified terminal UI. It enables users to interact with service logs, filter messages, and control development environments directly via Model Context Protocol integration.

A lightweight service that enables AI assistants to execute AWS CLI commands through the Model Context Protocol (MCP), allowing AI tools to retrieve AWS documentation and interact with AWS services.

Enables AI assistants to create, monitor, and manage computational tasks through GA4GH Task Execution Service (TES) functionality. Provides seamless access to TES-compliant services for executing bioinformatics and scientific computing workflows.

Provides MCP tools for common developer operations like file outlining, git commit context, test summary, process management, and waiting for conditions, returning structured JSON to reduce token usage.

Enables AI assistants to execute AWS CLI commands and retrieve service documentation through the Model Context Protocol. It supports Unix pipes for output filtering and provides pre-defined prompt templates for common cloud management tasks.

Keeps long Bash command output out of an agent's context by saving it to a file and returning only a short summary with the path, so the agent can read that file later or query it with yes/no questions and relevance searches that avoid dumping source text back into context.

A desktop management platform for plugin management, log querying, service control, and packaging/deployment, with an MCP protocol bridge for AI clients.

A minimal demonstration of an MCP service and client that integrates with the qwen API interface, showing basic functionality of a Model Control Protocol implementation.

Enables Claude Code to delegate tasks to persistent GitHub Copilot CLI sessions via MCP, inspect pending tool requests, and approve each operation once or deny it while continuing multi-turn context. It bridges ACP runtime sessions locally over stdio without a public service or background daemon.