Retro Diffusion Pixel Art MCP

Create Custom Style

create_user_style

Create a user style from the public RD Pro template using /v1/styles.

Use style_reference_images and style_reference_caption for style-level references. These are baked into the custom style and are not the same as per-inference reference_images.

Input Schema

TableJSON Schema

Name	Required	Description
`name`	Yes	Display name for the custom style.
`min_width`	No	Force a fixed width (96-256); must be provided together with min_height.
`min_height`	No	Force a fixed height (96-256); must be provided together with min_width.
`rd_api_key`	No	RetroDiffusion API key (rdpk-...) for this call only; overrides session or header auth.
`style_icon`	No	Icon name for the style (e.g. 'sparkles').
`description`	No	Short description of the custom style.
`force_palette`	No	Always apply palette constraining for this style.
`force_bg_removal`	No	Always remove backgrounds for this style.
`llm_instructions`	No	Instructions for the prompt-expansion LLM when this style is used.
`reference_images`	No	Alias for style_reference_images on this tool; prefer style_reference_images and never provide both.
`reference_caption`	No	Alias for style_reference_caption on this tool; prefer style_reference_caption and never provide both.
`apply_prompt_fixer`	No	Let the API tidy prompts automatically for this style (default true).
`user_prompt_template`	No	Prompt template for the style; must contain the {prompt} token.
`style_reference_images`	No	Style-level reference image(s), base64, baked into the custom style (max 1 via the public API).
`style_reference_caption`	No	Caption describing the style reference image(s).
`expanded_llm_instructions`	No	Extended instructions for the prompt-expansion LLM.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.2/5.0

Behavior3/5

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

Annotations indicate a mutating, non-idempotent, non-destructive operation. Description adds that the style is created from a public template and that references are baked in. However, it does not disclose potential side effects like name uniqueness, whether existing styles are overwritten, or auth requirements beyond the API key parameter.

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?

Three sentences, no wasted words. First sentence gives purpose and endpoint, second provides usage guidance for key parameters, third clarifies an important distinction. Front-loaded and efficient.

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?

Given the rich input schema (descriptions for all 16 parameters) and presence of an output schema, the description provides the core context needed. It could mention the min_width/min_height pairing requirement, but the schema's descriptions already cover that. Overall adequate.

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?

With 100% schema description coverage, the baseline is 3. The description adds value by clarifying the purpose of style_reference_images/caption (baked into custom style vs per-inference) and noting that reference_images/caption are aliases but to prefer the style_ prefixed ones. This goes beyond the schema's descriptions.

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?

Clearly states the tool creates a user style from a public RD Pro template. The verb 'create' and resource 'user style' are specific, and it distinguishes style-level references from per-inference references, differentiating it from sibling tools like create_inference.

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?

Provides guidance on using style-level parameters (style_reference_images/caption) and distinguishes them from per-inference reference_images. However, it does not explicitly name alternative tools like update_user_style or delete_user_style for when modification or deletion is needed.

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

All Retro Diffusion Pixel Art MCP tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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

Need Help?