Retro Diffusion Pixel Art MCP

List Styles

list_available_styles

Read-onlyIdempotent

List publicly available styles from /v1/styles/selector. Use this to discover valid prompt_style values plus style metadata such as require_input_image and supports_reference_images.

Input Schema

TableJSON Schema

Name	Required	Description
`tab`	No	Filter styles by tab: tab:image, tab:animation, tab:advanced-animation, or tab:tileset.
`model`	No	Filter styles by model: rd_fast, rd_plus, rd_pro, or rd_mini.
`rd_api_key`	No	RetroDiffusion API key (rdpk-...) for this call only; overrides session or header auth.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already provide read-only, idempotent, non-destructive hints. The description adds valuable context by naming the specific endpoint and the type of metadata returned (e.g., require_input_image), which enhances transparency beyond annotations.

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 only two sentences, concise and front-loaded with the main action. Every word serves a purpose, no unnecessary details.

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 tool has optional filters and an output schema, the description covers the essential purpose and output values. It could mention that filtering by tab and model is possible, but that is covered by the schema. The description is complete enough for an agent to correctly use the tool.

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 coverage is 100% with each parameter having a description. The description does not add parameter-specific details, but the baseline is 3 since the schema is comprehensive. The description's mention of 'style metadata' indirectly relates to output, not input parameters.

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 tool lists publicly available styles from a specific endpoint and is used to discover valid prompt_style values and style metadata. This purpose is specific and distinguishes it from sibling tools like create_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 explicitly states when to use this tool: to discover prompt_style values and metadata. While it doesn't list alternatives or when not to use, the context of sibling tools makes the usage clear.

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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