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Glama

Upscale, expand, empty a room, vectorize

luw_image_tools

Perform one-step image edits: upscale 2x/4x/8x, expand tightly cropped architectural photos, remove furniture from rooms, and vectorize photos or drawings into clean SVG files.

Instructions

One-step image utilities, 1 credit each:

  • upscale: enhance quality and enlarge 2x/4x/8x (Photo Enhance AI)

  • expand: outpaint a tightly cropped architectural photo to a wider view (Expand AI)

  • remove_furniture: empty a furnished room, keeping walls, floor and windows (Remove Furniture AI)

  • vectorize: convert a photo or drawing into a clean SVG vector (Vector AI)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageYesInput image (https:// URL, local file path, or data: URI).
scaleNoupscale only: 2, 4 or 8 (default 2).
formatNoOutput image format.
operationYes
precisionNoHow strictly to keep the input's structure: 90 = precise, 75 = balanced, 40 = creative.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare the safety profile (readOnly=false, destructive=false, idempotent=false, openWorld=true). The description adds value beyond them: the per-operation cost ('1 credit each') and the preservation guarantee for remove_furniture ('keeping walls, floor and windows'). It does not disclose return format or async/result-retrieval behavior, which matters for an openWorld generation tool.

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?

A front-loaded one-line summary followed by four tight bullets, each naming an operation and its effect. No filler; every clause earns its place.

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?

With no output schema, the description should carry the return/result story, and it does not say how output is retrieved (though a luw_get_result sibling exists). Otherwise it is complete for a 4-operation, 5-parameter utility tool, with cost and operation semantics covered.

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 coverage is 80%, so the baseline is 3. The description usefully expands the 'operation' enum values (which the schema leaves undocumented) and confirms scale is upscale-only. However, it says nothing about 'precision' or 'format', and precision's applicability to a specific operation is left ambiguous.

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

Purpose4/5

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

The description states a specific resource (one-step image utilities) and enumerates each of the four operations with a concrete verb and effect ('upscale: enhance quality and enlarge 2x/4x/8x', 'remove_furniture: empty a furnished room'). An agent can tell what each operation does. It stops short of distinguishing this multi-op tool from siblings like luw_edit_image or luw_render.

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

Usage Guidelines4/5

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

Each bullet supplies the condition that selects it ('outpaint a tightly cropped architectural photo', 'empty a furnished room'), which is genuine usage context. It never names alternatives or exclusions relative to the sibling image tools, so it is clear-but-not-routing.

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