Retro Diffusion Pixel Art MCP

Get Style Usage

get_style_usage

Read-onlyIdempotent

Explain how to use a public prompt_style from the RetroDiffusion API.

Use this before create_inference if you are unsure whether a style expects input_image, supports per-inference reference_images, or whether you meant style-level style_reference_images.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`rd_api_key`	No	RetroDiffusion API key (rdpk-...) for this call only; overrides session or header auth.
`prompt_style`	Yes	Style id from list_available_styles (e.g. 'rd_fast__default', 'rd_pro__isometric', or a custom 'user__...' style).

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. The description adds behavioral context about what the tool explains (style expectations), going beyond annotations to clarify its role as a consultation 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?

Two sentences: first states purpose, second gives usage guidance. No superfluous words; every sentence earns its place. Front-loaded with the essential action.

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

Completeness5/5

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

Given the presence of output schema and comprehensive annotations, the description provides all necessary context: it explains the tool's purpose and when to use it, fitting the complexity of a style usage query.

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?

Input schema has 100% coverage with descriptions for both parameters. The description does not repeat param details but adds meaningful context about how the tool's output informs the usage of those parameters in create_inference, thus enhancing semantic understanding.

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?

The description clearly states 'Explain how to use a public prompt_style', using a specific verb and resource. It distinguishes from sibling tools like 'create_inference' and 'list_available_styles' by focusing on usage explanation.

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

Usage Guidelines5/5

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

Directly advises 'Use this before create_inference if you are unsure...' and lists specific scenarios (input_image, reference_images, style_reference_images). Provides explicit when-to-use guidance and implies alternative by referencing create_inference.

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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TDQS

A4/5.0

Disambiguation4/5

Most tools have clearly distinct purposes (e.g., create_inference vs start_inference_job vs get_inference_result). However, the difference between create_user_style/update_user_style and per-inference references could still cause confusion, and list_available_models/list_available_styles overlap slightly.

Naming Consistency5/5

All tool names consistently follow a verb_noun pattern (e.g., create_inference, get_balance, list_edit_tools, delete_user_style). No mixing of camelCase or other styles, making the surface highly predictable.

Tool Count5/5

With 20 tools covering authentication, inference (sync/async), styles, editing, cost estimation, and status, the count is appropriate for a pixel art generation API. Each tool addresses a distinct need without bloat.

Completeness4/5

The tool set covers the full lifecycle: auth, cost estimation, synchronous/async generation, style management, editing, and result retrieval. A minor gap is the lack of a tool to list or manage user styles (e.g., get_user_styles), but this is non-critical for core workflows.

Resources

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