An MCP server that enables control of Philips Hue lights through Claude and other LLM interfaces using the OpenHue CLI.
Most AI and media services work still happens through a UI a human drives. Openhue MCP server moves it into the conversation instead. An MCP server that enables control of Philips Hue lights through Claude and other LLM interfaces using the OpenHue CLI.
The server publishes 4 tools. What each one is for:
macOS — ~/Library/Application Support/Claude/claude_desktop_config.jsonWindows — %APPDATA%\Claude\claude_desktop_config.jsonBuilding — The Building tool exposed by this serverRunning — The Running tool exposed by this serveropenhue/cli on a container image is all you need. Most clients run it directly, so configuration is a few lines and a restart.
Plenty of AI and media services servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Openhue's toolset — macOS, Windows, Building and 1 more — is a fair guide to whether it matches your workflow. It is maintained by lsemenenko; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| macOS | ~/Library/Application Support/Claude/claude_desktop_config.json |
| Windows | %APPDATA%\Claude\claude_desktop_config.json |
| Building | The Building tool exposed by this server. |
| Running | The Running tool exposed by this server. |
{
"mcpServers": {
"openhue": {
"command": "docker",
"args": ["run", "-i", "--rm", "openhue/cli"]
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.