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Interior design (Interior AI)

luw_interior_design

Luw.ai Interior AI: redesign an interior photo — home, office, shop, hotel — in any style while keeping the room's architecture. Restyles furniture, materials, colors and lighting.

Instructions

Luw.ai Interior AI: redesign an interior photo — home, office, shop, hotel — in any style while keeping the room's architecture. Restyles furniture, materials, colors and lighting. Set empty_room=true to furnish an empty room or turn it into another room type (virtual staging). Costs 1 credit per variation.

Input Schema

NameRequiredDescriptionDefault
seedNoFix for reproducible results.
imageYesPhoto of the room (https:// URL, local file path, or data: URI).
engineNoLuw.ai model: aria (default) or symphony (Symphony-3).
formatNoOutput image format.
promptNoWhat you want, in plain language.
stylesNoDesign style names, e.g. ["Scandinavian"]; for Japandi use ["Japanese Design", "Scandinavian"]. Case-sensitive; see luw_list_options.
precisionNoHow strictly to keep the input's structure: 90 = precise, 75 = balanced, 40 = creative.
room_typeNoRoom type, e.g. "Living Room", "Kitchen", "Bedroom", "Home Office" (see luw_list_options kind="interior_types").
empty_roomNoFurnish from scratch / convert the room to room_type (Fill Empty Room mode).
persona_idNoPersona whose saved images and style knowledge to use.
variationsNoNumber of alternative designs (1-4, default 1); each is billed as a generation.
enhance_promptNoLet Luw.ai's prompt enhancer expand a short prompt.
style_referenceNoStyle-transfer source: an image whose look to copy (https:// URL, local file path, or data: URI), or "persona" to use slot 1 of persona_id.
reference_imagesNoUp to 6 reference images (furniture, materials, products, mood boards) to draw from.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.3.2
    • changedInput schema / properties / persona_id / description
      Previous value: -"Persona whose saved images/knowledge to use (see luw_personas)."New value: +"Persona whose saved images and style knowledge to use."
    • changedInput schema / properties / styles / description
      Previous value: -"Design style names, e.g. [\"Japandi\"]. Case-sensitive; see luw_list_options."New value: +"Design style names, e.g. [\"Scandinavian\"]; for Japandi use [\"Japanese Design\", \"Scandinavian\"]. Case-sensitive; see luw_list_options."
  2. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations cover the safety profile (readOnly=false, idempotent=false, openWorld=true), and the description adds value beyond them: credit cost per variation and the guarantee that room architecture is preserved while surfaces are restyled. It stops short of describing result retrieval or failure behavior, so not a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three dense sentences, zero waste, with the core verb and scope front-loaded before the mode hint and the cost note.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 14-parameter, mutation-capable generation tool with no output schema, the description covers the operating modes, what is preserved, and billing. It omits how results are retrieved (likely via luw_get_result) and any latency/rate context, leaving a small gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and already documents all 14 parameters, so the baseline is 3; the description goes beyond it by explaining the empty_room mode semantics (virtual staging / room-type conversion) and by pointing to luw_list_options for style and room_type values.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('redesign an interior photo — home, office, shop, hotel — in any style while keeping the room's architecture') and enumerates what is restyled (furniture, materials, colors, lighting), which clearly separates it from siblings like luw_exterior_design, luw_landscape_design and luw_remove_furniture.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives a mode condition ('Set empty_room=true to furnish an empty room or turn it into another room type') but never states when to prefer this over sibling tools such as luw_exterior_design, luw_render or luw_edit_image. Usage is implied by the interior-photo scope rather than explicitly routed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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