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Luw MCP Server

Luw MCP Server

Official
by Luvi-io
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TypeScript
Hybrid

Empty a room

luw_remove_furniture

Luw.ai Remove Furniture AI: remove the furniture from a room photo, keeping walls, floor and windows. Costs 1 credit.

Instructions

Luw.ai Remove Furniture AI: remove the furniture from a room photo, keeping walls, floor and windows. Costs 1 credit.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "image": "<image>"
    },
    "name": "luw_remove_furniture"
  }
}

Input Schema

NameRequiredDescriptionDefault
imageYesPhoto of the furnished room (https:// URL, local file path, or data: URI).
formatNoOutput image format.
precisionNoHow strictly to keep the input's structure: 90 = precise, 75 = balanced, 40 = creative.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.3.2

TDQS

A3.7/5.0
Behavior4/5

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

Annotations only cover the generic safety profile (not read-only, open-world, non-idempotent, non-destructive). The description adds genuinely new behavior: the operation is content-preserving (walls, floor, windows kept) and costs 1 credit. It stops short of describing the return value or delivery mechanism, but the credit cost and preservation guarantee are real added value.

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

Conciseness4/5

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

One front-loaded sentence carrying the action, the preservation guarantee and the cost, with zero filler. The only slack is the redundant branded prefix that restates the tool name.

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 3-parameter, fully documented schema with no output schema, an agent has what it needs: accepted input formats, output format, and the semantics of precision. The cost note is useful. Slightly incomplete in not saying how the resulting image is returned (directly vs. via luw_get_result), which matters given the sibling set.

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

Parameters3/5

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

Schema description coverage is 100%, so all three parameters (image, format, precision) are already documented with examples including the accepted input forms and the precision values. The description adds nothing on parameters, which is acceptable at full coverage — the baseline of 3 applies.

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 (remove) and resource (furniture) plus the scope constraint (from a room photo, keeping walls, floor and windows). An agent can distinguish it from siblings like luw_interior_design or luw_edit_image without opening any schema. The 'Luw.ai Remove Furniture AI' prefix is brand boilerplate but harmless.

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

Usage Guidelines2/5

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

The description implies the input is a furnished room photo but never states when to choose this tool over siblings such as luw_background, luw_edit_image or luw_magic_wand. No exclusions, prerequisites, or alternative routing are given.

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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