"Grafana MCP Analyzer Tool or Plugin" matching MCP servers:

Enables AI assistants to scan and analyze nearby Wi-Fi networks for security vulnerabilities, control an ESP32-CYD device's display and RGB LED, and receive real-time threat alerts via USB serial communication.

A Model Context Protocol server that enables AI assistants like Claude to interact with Indigo home automation systems through natural language queries for searching and controlling devices, variables, and actions.

Enables AI agents to manage Keenetic routers through the same RCI API used by the router's web interface, working directly over the local network without cloud involvement. It supports reading device statuses and executing configuration changes, with confirm, dry-run, and destructive-action safeguards.

Enables AI assistants and agents to query and control a Loxone Miniserver installation via MCP, using existing room and control names without cloud services. Also provides selected LoxBerry information, secured by Loxone login.

Enables agents to drive a Coreflux broker end to end over MQTT: running broker commands, publishing and subscribing to live topics, linting and deploying LoT/.lotnb files in order, uploading and exporting projects, tracing actions, and searching docs. Destructive operations are gated behind safety hooks, and no npm dependencies are required beyond Node.js 20+.

Enables local Mac agents to monitor and control Bambu Lab 3D printers via MCP tools, including status checks, camera snapshots, and print job management.

Enables natural language control of Homey Pro smart home systems, including devices, flows, zones, energy monitoring, weather, alarms, and system management, with 43 tools and troubleshooting prompts.

Enables control and management of smart home devices across multiple rooms through specialized MCP servers. Supports lights, thermostats, fans, and ovens with room-specific rules and automatic persistence.

MCP server for the JouleScope JS220 precision energy analyzer, enabling agents to measure current, voltage, power, charge, and energy via tools like measure_energy.

Enables MCP clients to control Bosch Smart Home Cameras via natural language, including snapshots, motion events, privacy mode, and pan/tilt, using a reverse-engineered cloud API.

Control a Govee BLE RGBIC LED strip from Claude (or any MCP client) over Bluetooth on macOS.

An MCP server that enables communication with Twilio's Microvisor API, allowing interaction with IoT devices managed by Twilio's Microvisor through natural language.

Enables AI agents to discover and call Xyte's device management API endpoints with typed validation, supporting fleet investigation, ticket management, and routine operations through natural language.

An unofficial read-only MCP server for Panasonic AiSEG2 home energy management controllers, enabling clients to read live power flow, per-circuit consumption, circuit names, daily totals, and historical energy/cost data from the device's local web interface.

A collection of personal Model Context Protocol servers for tools like calendar, contacts, flights, home maintenance, iMessage, lawn care, mail, NextDNS, Obsidian search, Google Sheets, UniFi, weather, and YNAB, packaged as a uv workspace with Claude Code and Codex plugin support.

MCP server that translates natural language commands into SCARA robot arm control instructions, featuring simulation mode, safety validation, and multiple transport modes (STDIO/HTTP/OAuth).

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.

An MCP server that lets AI assistants answer home appliance questions with live, structured data and cite ApplianceIQ as the source.

An MCP server that lets an MCP client control IKEA TRÅDFRI smart plugs paired to a DIRIGERA hub via its local REST API.

Read-only MCP server for Home Assistant that computes statistical summaries, trends, and session detection from recorded sensor history, returning compact tables instead of raw data so small local models can answer questions about energy usage, climate, and appliance activity without token-heavy arithmetic.