"How to create a remote directory" matching MCP servers:

Enables natural-language control of DK-META2 devices through local BLE for vibration, linear extension, and rotation. It also supports scanning, connecting, status checks, and safe stop/disconnect operations without cloud access.

Enables pushing text from an AI conversation to a low-cost BLE e-ink shelf label via an MCP tool, with rich-text markup, card-style letter layouts, and real-time SSE delivery to a phone control page. Also lets clients check current screen content, update time, push history, and whether a phone is online.

An MCP server that lets AI agents control Android TVs via ADB, including searching and playing content across streaming services, managing power and volume, and sending remote control commands.

An MCP server that enables AI assistants to control TVs on a local network through natural language commands. It currently supports Roku devices, allowing users to launch apps, manage playback, and navigate menus.

Vision-capable MCP server for controlling Amazon Fire TV through Home Assistant, providing screenshot-guided observation, remote commands, and app/URI actions via authenticated REST calls.

Enables natural language interaction with Unfolded Circle Remote 3/Remote Two for configuration, button mapping, UI editing, and backup/restore.

Enables reading and configuring an Unfolded Circle Remote Two or Remote 3 through the official Core REST API, exposing activities, touchscreen pages, profiles, integration entities, and physical-button mappings to MCP clients.

Enables Claude, Claude Desktop, and Cursor to read sensor values, monitor device status, and execute real-world actions through Vento boards and agents.

Turns AI assistants like Claude and ChatGPT into a remote control for LG webOS smart TVs, enabling power control, volume, app launching, input switching, and more, all locally without cloud APIs.

Enables remote control of a StackChan robot via MQTT and MCP, allowing AI clients to switch its facial expressions and trigger camera photos for visual feedback.

Enables local, cloud-free control of Daikin air conditioners from MCP clients, letting assistants read unit state and adjust power, mode, temperature, fan, louvres, and special modes over the unit's local HTTP API.

Enables autonomous AI agents to safely inspect, configure, and visually validate remote Home Assistant dashboards, automations, and scripts.

Enables remote control of a virtual LUMEI lamp via Claude, allowing users to adjust power, brightness, mode, tracking, and view product info or battery status.

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 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.