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Retro Diffusion Pixel Art

by ai.retrodiffusion

Update Custom Style

update_user_style
Idempotent

Update a public RD Pro user style using /v1/styles/{style_id}.

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
NameRequiredDescriptionDefault
nameNoNew display name for the custom style.
style_idYesStyle id — the internal id or the prompt_style/public id (e.g. user__mystyle_ab12).
min_widthNoForce a fixed width (96-256); must be provided together with min_height.
min_heightNoForce a fixed height (96-256); must be provided together with min_width.
rd_api_keyNoRetroDiffusion API key (rdpk-...) for this call only; overrides session or header auth.
style_iconNoIcon name for the style (e.g. 'sparkles').
descriptionNoShort description of the custom style.
force_paletteNoAlways apply palette constraining for this style.
force_bg_removalNoAlways remove backgrounds for this style.
llm_instructionsNoInstructions for the prompt-expansion LLM when this style is used.
reference_imagesNoAlias for style_reference_images on this tool; prefer style_reference_images and never provide both.
reference_captionNoAlias for style_reference_caption on this tool; prefer style_reference_caption and never provide both.
apply_prompt_fixerNoLet the API tidy prompts automatically for this style (default true).
user_prompt_templateNoPrompt template for the style; must contain the {prompt} token.
style_reference_imagesNoStyle-level reference image(s), base64, baked into the custom style (max 1 via the public API).
reset_forced_dimensionsNoClear previously forced dimensions; do not combine with min_width/min_height.
style_reference_captionNoCaption describing the style reference image(s).
expanded_llm_instructionsNoExtended instructions for the prompt-expansion LLM.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A3.9/5.0
Behavior3/5

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

Annotations (readOnlyHint=false, destructiveHint=false, idempotentHint=true) are consistent with the description of an update operation. The description adds minimal behavioral context beyond what annotations provide, such as clarifying that style-level references are baked in. No contradictions.

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?

The description is extremely concise at two sentences, with the main action front-loaded and a clarifying second sentence. No redundant or extraneous information.

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?

Given 18 parameters and an output schema, the description is minimal. It addresses the key distinction between style-level and per-inference references, but lacks broader update semantics (e.g., whether omitted fields are left unchanged) and usage examples. Adequate but not complete.

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?

Schema coverage is 100%, but the description adds value by clarifying the distinction between style_reference_images and per-inference reference_images, noting that min_width/min_height must be provided together, and that reset_forced_dimensions should not be combined with them. This goes beyond the schema's individual parameter 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?

The description clearly states the action ('Update') and the resource ('public RD Pro user style'), and the tool name distinguishes it from siblings like create_user_style and delete_user_style. The purpose is unambiguous.

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 description provides some guidance on when to use style_reference_images/caption versus per-inference reference_images, but it does not explicitly state when to use this tool over alternatives, nor does it mention prerequisites or exclusions.

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

A4.1/5.0
Disambiguation5/5

Each tool targets a distinct operation: authentication, inference (sync/async/edit), style management, listing, and utility. Even similar tools like create_inference and start_inference_job are clearly differentiated by synchronous vs async execution.

Naming Consistency5/5

All tool names follow the same verb_noun snake_case pattern (e.g., create_inference, get_balance, list_available_styles). No mixed conventions or inconsistent verb forms.

Tool Count4/5

With 19 tools, the count is slightly above the typical 3-15 range, but each tool serves a specific and necessary function for pixel art generation, editing, and management, so it remains well-scoped.

Completeness5/5

The tool set covers the full workflow: authentication, cost estimation, synchronous and async generation, style CRUD, edit tools, pixel fixing, and system status. No obvious gaps for the intended domain.

Resources