Retro Diffusion Pixel Art MCP

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

Name	Required	Description
`name`	No	New display name for the custom style.
`style_id`	Yes	Style id — the internal id or the prompt_style/public id (e.g. user__mystyle_ab12).
`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).
`reset_forced_dimensions`	No	Clear previously forced dimensions; do not combine with min_width/min_height.
`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

A4.3/5.0

Behavior4/5

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

The description adds context beyond annotations by explaining that style references are baked into the style, that it uses a public API endpoint, and that max 1 image is allowed via the public API. Annotations already indicate non-readOnly and non-destructive, so consistency is maintained.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences cover purpose and key parameter guidance. The first sentence is front-loaded with action and resource. Could benefit from structuring, but it's 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 18 parameters, 100% schema coverage, and output schema exists, the description covers key behavioral aspects and parameter relationships. It does not explain prerequisites like ownership of the style, but it's largely complete for the tool's complexity.

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 coverage, baseline is 3. The description adds value by explaining the relationship between style_reference_images and reference_images (aliases), the requirement for user_prompt_template to contain {prompt}, and the baking-in of style references.

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 'Update a public RD Pro user style' using the specific endpoint. The verb 'Update' and resource are explicit, and it distinguishes from siblings create_user_style and delete_user_style.

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?

The description provides guidance on using style_reference_images and style_reference_caption for style-level updates, differentiating them from per-inference reference_images. However, it does not explicitly contrast this tool with create/delete or specify when not to use it.

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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