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Glama

Seamless pattern / texture (Pattern AI)

luw_generate_pattern

Luw.ai Pattern AI: generate a seamless, tileable pattern or texture — tiles, wallpaper, fabric, terrazzo, wood, stone — ready to repeat across a surface. Costs 1 credit.

Instructions

Luw.ai Pattern AI: generate a seamless, tileable pattern or texture — tiles, wallpaper, fabric, terrazzo, wood, stone — ready to repeat across a surface. Costs 1 credit.

Input Schema

NameRequiredDescriptionDefault
sizeNoOutput size in px. aria: 512 or 768 (default); symphony: 512 or 1024 (default).
imageNoOptional reference image (https:// URL, local file path, or data: URI).
engineNoLuw.ai model: aria (default) or symphony (Symphony-3).
formatNoOutput image format.
promptYesThe pattern, e.g. "blue and white Moroccan zellige tiles".
enhance_promptNoLet Luw.ai's prompt enhancer expand a short prompt.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

Annotations already declare the safety profile (readOnlyHint=false, destructiveHint=false, openWorldHint=true, idempotentHint=false), so the bar is lower. The description adds the 1-credit cost and the seamlessness guarantee, but says nothing about generation being asynchronous or that the result must be fetched via luw_get_result, which is the most consequential behavioral fact for a generator.

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 single dense sentence leads with the tool identity and core capability, followed by a short cost note. No filler, and the most important information (what it produces) is front-loaded.

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

Completeness3/5

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

For a 6-parameter generator with no output schema, the description omits how the result is obtained (async polling via luw_get_result) and any prompt-authoring expectations despite a 4000-char prompt field. What it does say is accurate, but the operational loop around the call is incomplete.

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% and the schema already documents engine/size interactions, reference image, format, and prompt. The description adds no parameter meaning beyond what the schema provides, so baseline 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 and resource ('generate a seamless, tileable pattern or texture') and enumerates concrete domains (tiles, wallpaper, fabric, terrazzo, wood, stone). This is clearly distinguishable from the generic sibling luw_generate_image without opening either schema.

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?

The phrase 'ready to repeat across a surface' implies the use case (surface-covering textures), but there is no explicit when-to-use guidance and no alternative named — an agent must infer that luw_generate_image is for non-tiling images. The credit-cost note is informative but not routing guidance.

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