"Python context management and dependency solving in ComfyUI" matching MCP servers:

MCP server for AI-controlled GPU VRAM management in ComfyUI, allowing Claude to monitor and control GPU state, manage model offloading, and start/stop ComfyUI.

An MCP server for unified cost tracking and analysis across AWS, OpenAI, and Anthropic. It enables users to query expenditures, compare costs across providers, and analyze usage trends through natural language.

Windows tray process that exposes local ambient context (presence, foreground app, battery, etc.) as MCP tools with privacy classification and opt-in controls.

Enables integration between Claude AI and Nasuni environments to monitor appliance health, manage volumes and shares, and generate comprehensive infrastructure reports. Users can interact with the Nasuni Management Center through natural language to track performance metrics and summarize system notifications.

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

An MCP server that records agent execution metrics and exposes a Context Window Explorer to visualize exactly what entered the model's context window across sessions, tokens, and tool calls.

Context window usage estimation for AI coding agents via MCP, enabling proactive state preservation before compaction.

Enables assistants to read-only explore and analyze Grafana dashboards and data sources, execute queries against Prometheus and Azure Log Analytics, and run guided investigation workflows.

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 access real-time Windows PC context including active window, system performance, screen time, productivity analytics, and historical usage through MCP tools.

Provides a read-only interface to Kubernetes clusters, enabling LLMs to list pods, get pod status and logs, fetch deployment manifests, and perform pod health analysis with resource trend tracking.

Provides unified project context and monitoring capabilities including project health metrics, build diagnostics, Git integration, and infrastructure validation to initialize development sessions with comprehensive project information.

A comprehensive system that helps organizations track, manage, and respond to security vulnerabilities effectively through features like vulnerability tracking, user management, support tickets, API key management, and SSL certificate management.

Enables plant operators to query alarm data, asset information, trends, correlations, and operator recommendations through 14 tools, forming a core part of a multi-MCP enterprise operations copilot.

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 registration, monitoring, and control of IoT devices via AI agents, with local storage and no cloud API key required.

Gives your AI assistant full control of a Discord server: 148 tools for chat, moderation, automod, events, and administration, up to building a complete community server from one paragraph. Every destructive action previews first and waits for your confirmation.

Enables AI assistants to query and analyze AI agent sessions from observability providers like Shepherd (AIOBS) and Langfuse, allowing users to debug agent runs, compare sessions, track performance, and analyze LLM usage patterns.

An MCP server that provides comprehensive monitoring and management capabilities for Teradata Workload Management (WLM), enabling tasks like performance troubleshooting, emergency throttling, and scheduled maintenance through 41 tools and 39 resources.

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