Nanoleaf
MCP ServerFree** - Control Nanoleaf smart lights through MCP - turn on/off, adjust brightness, change colors, set effects, and discover devices
Capabilities6 decomposed
mcp-based device discovery and enumeration
Medium confidenceDiscovers and enumerates all Nanoleaf devices on the local network by querying the Nanoleaf API endpoint, returning structured device metadata including device IDs, names, model types, and firmware versions. Implements MCP server protocol to expose discovery as a callable tool, enabling LLM agents and CLI clients to programmatically detect available Nanoleaf hardware without manual configuration.
Exposes Nanoleaf device discovery as an MCP tool, allowing LLM agents to dynamically discover hardware at runtime rather than requiring hardcoded device IDs; integrates directly with the Nanoleaf local API without requiring cloud authentication
Simpler than REST-based discovery approaches because it abstracts API complexity into a single MCP tool call that agents can invoke naturally in conversation
power state control with mcp tool binding
Medium confidenceToggles Nanoleaf devices on and off by sending HTTP POST requests to the Nanoleaf API's power endpoint, with state changes reflected immediately on the device. Implements MCP tool schema binding that maps natural language intents (e.g., 'turn on the lights') to structured API calls with device ID and power state parameters, enabling agents to control power without explicit API knowledge.
Wraps Nanoleaf power API in MCP tool schema, allowing agents to invoke power control through natural language without understanding HTTP semantics; integrates parameter validation at the MCP layer to catch invalid device IDs before sending API requests
More agent-friendly than raw REST API calls because MCP tool schema provides structured parameter validation and natural language grounding, reducing agent hallucination around API details
brightness adjustment with granular control
Medium confidenceAdjusts Nanoleaf device brightness on a 0-100 scale by sending HTTP requests to the brightness endpoint, supporting both absolute brightness values and relative adjustments (increase/decrease by percentage). Implements MCP tool binding with parameter constraints (0-100 range) enforced at the schema level, enabling agents to set precise brightness levels or make incremental adjustments without manual range validation.
Enforces brightness range validation (0-100) at the MCP tool schema level, preventing agents from sending out-of-range values to the API; supports both absolute and relative adjustment modes within a single tool, reducing the need for multiple tool definitions
More flexible than simple on/off control because it enables fine-grained brightness adjustment; more agent-safe than raw API access because schema-level range validation prevents invalid requests
color control with hsl/rgb conversion
Medium confidenceChanges Nanoleaf device colors by accepting HSL (Hue, Saturation, Lightness) or RGB color inputs and converting them to the Nanoleaf API's native format before sending HTTP requests. Implements color space abstraction at the MCP layer, allowing agents to specify colors in human-friendly formats (e.g., 'red', 'warm white') while the server handles conversion to device-compatible values.
Abstracts color space conversion (RGB/HSL to Nanoleaf native format) at the MCP server layer, allowing agents to use intuitive color names or standard color formats without understanding device-specific color encoding; supports multiple input formats (hex, named colors, HSL objects) through a single tool
More agent-friendly than raw API color control because it accepts multiple color input formats and handles conversion automatically; more intuitive than device-native color values because agents can use standard color names or hex codes
effect/animation selection and activation
Medium confidenceActivates predefined lighting effects and animations on Nanoleaf devices by querying available effects from the device API and sending selection commands via HTTP POST. Implements effect enumeration at the MCP layer, allowing agents to discover supported effects dynamically and select them by name rather than numeric IDs, enabling natural language effect selection (e.g., 'set to breathing mode').
Dynamically enumerates device-specific effects from the Nanoleaf API and exposes them as selectable options in the MCP tool schema, allowing agents to discover and activate effects without hardcoded effect lists; supports natural language effect names mapped to device API identifiers
More flexible than static effect lists because it queries the device API to discover available effects at runtime; more agent-friendly than numeric effect IDs because it uses human-readable effect names
multi-device orchestration through mcp tool composition
Medium confidenceEnables coordinated control of multiple Nanoleaf devices through a single MCP server instance by composing individual device control tools into higher-level workflows. Agents can invoke multiple device-specific tools in sequence or parallel to create synchronized scenes (e.g., 'set all lights to warm white at 50% brightness'), with the MCP server managing device enumeration and routing commands to the correct devices.
Leverages MCP tool composition to enable multi-device orchestration without requiring a dedicated multi-device tool; agents decompose high-level intents (e.g., 'set all lights to warm white') into individual device control calls, with the MCP server providing device discovery to enable dynamic device enumeration
More flexible than device-specific control because agents can compose tools to target multiple devices; more agent-native than REST API batching because it relies on agent reasoning to decompose multi-device intents
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
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Best For
- ✓LLM agent developers building multi-device smart home orchestration
- ✓Home automation enthusiasts automating device inventory discovery
- ✓Teams building MCP-compatible smart home control systems
- ✓LLM agent developers building voice/chat-driven smart home control
- ✓Home automation builders integrating Nanoleaf into broader orchestration systems
- ✓Non-technical users controlling lights through conversational AI interfaces
- ✓Smart home developers building context-aware lighting automation
- ✓LLM agents implementing dynamic scene adjustments based on user preferences
Known Limitations
- ⚠Requires devices to be on the same local network — no cloud-based discovery
- ⚠Discovery latency depends on network responsiveness; may timeout on congested networks
- ⚠Returns only devices that respond to Nanoleaf API queries; offline devices are not detected
- ⚠No state verification — assumes API call succeeds; does not confirm actual device state change
- ⚠Power control is binary (on/off) — no gradual fade or scheduled power transitions
- ⚠Requires valid device ID; invalid IDs fail silently without error feedback to the agent
Requirements
Input / Output
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** - Control Nanoleaf smart lights through MCP - turn on/off, adjust brightness, change colors, set effects, and discover devices
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