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Cuvara

game-art-mcp

by Cuvara

art.ref.pack

Assemble a curated reference pack for AI generation by combining style constraints, canonical references, negative examples, and material palette to enforce consistent pixel-art output.

Instructions

Build a reference pack for generation — combines style constraints, canonical references, negative examples, and material palette.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoAsset type filter
categoryNoAsset category filter
materialNoMaterial type filter
max_negativeNoMaximum negative examples
max_canonicalNoMaximum canonical references
Behavior2/5

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

There are no annotations, so the description carries full responsibility for behavioral disclosure. It states that the tool 'builds' a pack, but does not indicate whether this is a pure computation, whether it persists anything, whether it modifies existing assets, or what side effects/side states are involved.

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 sentence with a front-loaded verb and resource, followed by a concise component list. No extraneous words or repetition, and the core purpose appears first.

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

Completeness2/5

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

With no output schema and no annotations, the agent still lacks essential operational context: what the pack is, what it returns, whether it saves or registers anything, and how optional filters combine into the final pack. The current text provides only a high-level summary, which is insufficient for a tool with five non-required parameters and nontrivial behavior.

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 each parameter is already documented. The description adds some conceptual grouping by mentioning 'canonical references', 'negative examples', and 'material palette', but it does not explain parameter interactions or the meaning of the filter parameters beyond what the schema provides. Baseline 3 is appropriate.

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 uses a specific verb ('Build') and resource ('reference pack') and clarifies the purpose ('for generation'). It enumerates the combined components, which separates it from single-purpose siblings like art.ref.canonical or art.ref.retrieve, though it does not name them explicitly.

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 'for generation' implies when the tool is appropriate, and the component list suggests an aggregating role. However, there are no explicit conditions, exclusions, or references to alternative tools such as art.ref.retrieve or art.ref.canonical, so the agent must infer usage context.

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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