"Guide to Executing and Reading Terminal Commands" matching MCP servers:

An MCP server for tracking and managing AI command usage history using a PostgreSQL database. It enables users to log, search, and view statistics for various AI-related commands and their execution contexts.

A VSCode and Cursor extension that captures real-time terminal output and exposes it to AI assistants via the Model Context Protocol. It enables agents to proactively monitor logs, command execution, and errors without requiring manual copy-pasting from the user.

Terminal\[X]MCP is a Model Context Provider (MCP) server that enables intelligent terminal automation through a multi-agent system with distributed specialized roles for command execution, security validation, and real-time monitoring.

Enables interaction with Google Cloud services including billing cost analysis, log querying, and metrics monitoring through natural language commands. Provides comprehensive tools for managing GCP resources, analyzing costs, detecting anomalies, and retrieving operational insights.

Enables AI agents to monitor Docker containers, system resources, ports, and logs in real-time.

Exposes a synthetic issue tracker and pipeline warehouse as callable tools so an agent can answer operational questions about tickets, pipeline health, runs, incidents, and governed metrics with every claim cited to the exact tool call it came from. All writes are proposal-only, requiring human approval through a gated apply path that logs each step for audit.

Enables named, authenticated callers to run read-only introspection of a Google Cloud project and to read and write per-tenant notes that stay isolated between callers. Every request is scope-checked against a reviewed policy, rate-limited per caller, and audited without ever logging argument values.

Enables querying and analysis of container terminal operations including vessel tracking, crane management, and productivity metrics through natural language interactions with a MySQL database.

A comprehensive MCP server providing advanced Redmine project analytics, web automation via Playwright, and iTunes music integration. It enables detailed tracking of sprint metrics, bug counts, and team performance through a token-optimized tool architecture.

Enables AI agents and users to manage workspace files, monitor system metrics, take persistent notes, and retrieve weather data via MCP tools and resources.

Connects AI agents to microsandbox for creating lightweight sandboxes, executing code, managing files, and monitoring resources.

Enables MCP-aware hosts to discover and call tools for listing authorized devices, sending commands, reading telemetry, managing sub-devices, and creating, updating, or deleting device schedules through the API.

Enables monitoring and control of the SST development process by streaming dev logs, managing Lambda invocations, and executing lifecycle commands like start, stop, and restart. It provides real-time access to event streams and deployment status directly through the Model Context Protocol.

Enables AI assistants to analyze Linux system crash dumps by automatically discovering dump files, matching kernels, and executing interactive crash analysis commands through the MCP protocol.

MCP Server for notify to telegram / weixin

Enables managing your Starlink terminal fleet through Claude AI using the Starlink Enterprise API, supporting terminal management, telemetry, data usage, and service line operations.

Enables AI agents to investigate backend incidents by executing runbooks that gather evidence from observability and storage systems.

Enables operators and MCP clients to perform read-only, multi-cluster Kubernetes diagnostics by correlating deployment inventory, approved Prometheus metrics, firing alerts, and reusable troubleshooting skills without executing cluster commands or modifying workloads. It exposes tools, resources, and prompts for deployment health checks, metric queries, alert inspection, and skill discovery.

Monitors development commands and exposes terminal output to Claude in real-time, allowing AI assistants to see errors, logs, and stack traces without copy-pasting.

Provides AI coding agents with tools to query, search, and analyze persisted HTTP request logs, enabling efficient debugging without terminal noise.