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    Read-only VMware vCenter/ESXi monitoring. 8 MCP tools for VM inventory, host status, datastore capacity, cluster info, alarms, events, and VM details. Code-level enforced safety — no destructive operations exist in the codebase. Supports vSphere 6.5–8.0. Works with local models via Ollama/LM Studio.
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    1,223 PyPI
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    MIT
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    A local MCP server that connects Yandex Direct advertising reports and safe campaign creation to AI agents, enabling natural-language analytics and protected campaign setup.
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    Identifies resource-intensive processes on macOS across CPU, memory, and network usage, with tools for detailed process filtering and system overview.
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    MIT
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    FastMCP server with read-only tools to monitor Redis — queue depths, Celery queue status, connected clients, server/memory info, and per-database key counts
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    MIT
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    MCP server for global news monitoring, media analysis and sentiment tracking via WorldNewsAPI — full-text search across 150+ countries, German/English sentiment analysis, top headlines, GL briefings, newspaper front pages and geo-search. API key required.
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    MIT
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    Enables LLMs to connect to factory PLC sensors, read register data, analyze predictive maintenance, and monitor energy consumption in industrial environments.
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    MIT
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    Monitors AWS infrastructure including EC2, RDS, S3, SQS, CloudWatch, and more through 19 tools, enabling natural language queries for infrastructure status.
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    Monitors Next.js development servers with intelligent error categorization, smart diagnostics, and performance tracking.
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    Bridges with an installed API monitoring application to enable launching and status checks, live background capture, process attach, filter-tree inspection, GUI actions, and opening, validating, decoding, searching, inspecting, exporting, and comparing saved captures and related metadata.
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    Enables AI agents to find, score, and monitor government contract opportunities across UK, EU, and US with AI-powered relevance scoring.
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    Enables natural language control of WeMo smart home devices for power, brightness, and device management through AI assistants. It features a high-reliability multi-phase discovery process to scan, monitor, and configure devices across a local network.
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    Enables AI assistants and local LLMs to interact with BirdNET-Go bioacoustic observatories, providing tools to query recent and historical bird detections, monitor station health, and retrieve audio clips.
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