LIFX API MCP Server

LIFX API MCP Server is a lightweight, local Model Context Protocol (MCP) server that exposes the full breadth of LIFX’s cloud‑based lighting control to AI assistants. By turning every LIFX API endpoint into a machine‑readable tool, the server lets natural language models (LLMs) query and manipulate smart bulbs, strips, tiles, and scenes without writing any HTTP code. Developers can therefore build conversational interfaces that “talk” to their home lighting system, embed dynamic lighting into workflows, or prototype voice‑controlled automation—all through the same MCP workflow that powers assistants like Claude.

Problem Solved

Smart lighting is powerful, but its REST API requires authentication tokens, JSON payloads, and knowledge of specific endpoints. For developers who already use MCP to connect LLMs to external services, adding LIFX support traditionally meant writing custom adapters or manual HTTP wrappers. The LIFX API MCP Server removes that friction by translating natural language into precise API calls and returning structured results. It also centralizes token handling, rate‑limiting logic, and error mapping so that the LLM can focus on intent rather than protocol plumbing.

What It Does

The server runs locally and exposes a set of tools that mirror the LIFX HTTP API. These tools include:

• Listing and querying lights or scenes – discover all devices, filter by selector, or inspect a single light’s state.
• State manipulation – set power, color, brightness, temperature, infrared level, or apply relative changes with .
• Effect execution – trigger built‑in animations such as breathe, pulse, move (for strips), morph, flame, clouds, sunrise, and sunset on Tile devices.
• Scene activation – load pre‑configured scenes by UUID, enabling instant mood or ambience changes.
• Batch operations – and let you update multiple lights in a single request, reducing network chatter.
• Utility helpers – parses color strings into LIFX’s expected format, while stops any running animations.

All tools expose rich JSON schemas for inputs and outputs, making them discoverable by MCP‑enabled clients. Prompt templates are also bundled to give LLMs pre‑written queries (e.g., ) that streamline common use cases.

Key Features & Value

Real‑World Use Cases

• Voice‑Controlled Home Automation – Integrate with assistants such as Claude or GPT‑based agents to control lights via natural language commands.
• Event Lighting Scripts – Write MCP scripts that trigger complex lighting sequences during parties or presentations, all driven by LLM‑generated scenarios.
• Ambient Feedback in Applications – An IDE or game can query the user’s current mood from a light state and adjust UI themes accordingly.
• Rapid Prototyping of IoT Flows – Developers can test lighting logic in a conversational sandbox before deploying to production.

Integration with AI Workflows

Once the MCP server is running, any MCP‑compatible client can query its tools through standard JSON RPC calls. The server’s resource URLs (e.g., ) become first‑class objects in the assistant’s context, allowing the LLM to reference them directly. Because each tool is self‑describing, the assistant can ask for clarification or suggest valid parameters without