"Setting up Minecraft (MCP) servers with Python and Visual Studio Code" matching MCP servers:

An external MCP server for diagnosing Minecraft servers via backup analysis, local runtime, or Docker runtime, offering tools for plugin inspection, log analysis, configuration linting, and performance diagnostics.

A lightweight, self-hosted MCP server that provides a small set of administration tools for a single Minecraft Java Edition server, including whitelist management, broadcasting, kicking, backups, and monitoring, with secure RCON-based operations and audit logging.

A Model Context Protocol server that provides AI assistants access to AWS CloudWatch Logs, enabling browsing, searching, summarizing, and correlating logs across multiple AWS services.

A Model Context Protocol (MCP) server for comprehensive monitoring and observability of systems using Langfuse.

Website monitoring for AI assistants. Visual Sentinel catches pages that look broken even when the site is up. DNS, SSL, speed and website checks need no account; monitors, incidents and alerts need an API key (Business and Agency plans).

Maintenance, recovery, and observability for Claude Code. CLI + MCP server + dashboard.

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 natural language queries for device support, including resolving device references, retrieving device details, historical metrics, and checking provisioning status. Maintains conversation context for multi-turn interactions.

Paid remote MCP for AI Studio Workspace with approval gate, structured receipts, audit logs, and reviewer-ready evidence, enabling workspace change checks, model run approval, spend estimation, and audit export.

A Model Context Protocol server for Zoho Site24x7 that uses a JavaScript sandbox to execute API calls, enabling management of monitors, accounts, and MSP/BU tenancy through natural language.

Enables real-time data streaming through Server-Sent Events with timestamp broadcasting and server monitoring capabilities. Optimized for deployment on Render.com with health checks and status endpoints.

Surface Claude Code token usage, estimated cost, and plan-limit status in any MCP client. Enables agents to query usage data from local logs and Anthropic API.

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.

Claude Code token usage and cost visibility for MCP-capable agents.

Enables automated review and analysis of network security controls (AWS Security Groups, Network ACLs) using natural language queries. Supports NSC configuration reviews, network segmentation testing, and identifying security gaps through conversational LLM interaction.

Enables real-time system monitoring and automation through MCP protocol with SSE transport, integrating with n8n workflows to check system health, query logs, and retrieve metrics from ABC system APIs. Supports natural language queries in Vietnamese and English for seamless system administration.

Enables analysis of AI agent traces to detect and fix failures using heuristic detectors, with no LLM calls required.

Provides tools for obtaining system CPU and memory statistics through FastAPI endpoints exposed via MCP.

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.

Provides read-only production observability through a small set of tools for health, job status, latest data, approved logs, and deployment metadata, enabling AI planning and review without a production write path.