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    A Model Context Protocol server that enables AI assistants to read and safely modify TP-Link Omada networks through capability-gated tools, with a default read-only profile and dry-run writes for security.
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    MIT
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    Enables AI to directly control SEGGER J-Link embedded debug probes via the Model Context Protocol for debugging and firmware management. Users can perform tasks like reading registers, analyzing memory, flashing firmware, and tracking RTT logs using natural language commands.
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    Enables AI assistants to interact with STM32 development boards via J-Link debugger using RTT communication, supporting connection, logging, memory operations, and firmware flashing through natural language.
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    Lets an AI assistant inspect and adjust TP-Link Omada WiFi networks by searching, describing, and calling any of roughly 1,650 Omada Open API operations through nine tools. It is read-only by default, refusing every non-GET request unless writes are explicitly unlocked, and even then returning a dry-run preview until a change is confirmed.
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    Enables AI assistants to debug embedded devices via SEGGER J-Link probes, including reading memory, flashing firmware, streaming RTT logs, and diagnosing crashes.
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    MIT
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    Enables Claude Code and other MCP clients to inspect and manage a TP-Link router over stdio, including device lists, DHCP leases and reservations, Wi-Fi settings, firmware and IP status, mesh nodes, and safe raw endpoint requests with confirmation for writes and secret masking.
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    MIT
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    Enables managing TP-Link Omada SDN controllers through MCP, exposing 72 tools for sites, devices, clients, networks, switch ports, firewalls, schedules, and site settings, including reboot and PoE scheduling.
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    MCP server that lets AI assistants monitor and manage TP-Link Omada controllers. It provides analytics and read-only queries plus write operations such as rebooting devices, blocking clients, and changing network or firewall settings.
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    MIT
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    Cortex is a Claude Code plugin that gives Claude persistent memory across sessions. A background miner extracts durable lessons (decisions, conventions, bug fixes, preferences) from your completed sessions, stores them as markdown in an Obsidian vault, and automatically surfaces relevant ones at the start of each new session via a query-tailored briefing.
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    Enables a local LLM to ask the user for guidance via a native desktop pop-up dialog, with configurable options and multiple-choice or free-text input.
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