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Retro Diffusion Pixel Art

by ai.retrodiffusion
OverviewDiscussionsAdmin

List Styles

list_available_styles
Read-onlyIdempotent

List publicly available styles from /v2/styles/selector. Use this to discover valid prompt_style values plus style metadata such as require_input_image and supports_reference_images.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_available_styles"
  }
}

Input Schema

NameRequiredDescriptionDefault
tabNoFilter styles by tab: tab:image, tab:animation, tab:advanced-animation, or tab:tileset.
modelNoFilter styles by model: rd_core, rd_fast, rd_plus (API only), rd_pro, or rd_mini.
rd_api_keyNoRetroDiffusion API key (rdpk-...) for this call only; overrides session or header auth.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / model / description
      Previous value: -"Filter styles by model: rd_fast, rd_plus, rd_pro, or rd_mini."New value: +"Filter styles by model: rd_core, rd_fast, rd_plus (API only), rd_pro, or rd_mini."
  2. First observed

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, non-destructive and openWorld, so the safety profile is covered. The description adds that results include metadata flags like require_input_image and supports_reference_images, which is genuinely useful behavioral context, but says nothing about pagination, caching, or the effect of the optional filters.

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, zero padding, with the resource and endpoint front-loaded and the practical use case second. Nothing here could be cut without losing information.

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?

With an output schema present, the description needn't enumerate return values, and it still calls out the two most decision-relevant metadata fields. All three optional parameters are documented in-schema, so an agent has what it needs to invoke the tool correctly; only filter-interaction and auth-fallback details are missing.

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 description coverage is 100%, with tab, model and rd_api_key all documented in-schema, so the baseline of 3 applies. The description adds no extra parameter meaning, such as how tab and model filters interact or whether omitting rd_api_key relies on session auth.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('List publicly available styles') and even names the backing endpoint (/v2/styles/selector) plus the downstream purpose (discovering valid prompt_style values). It is clearly distinct from sibling listers like list_available_models or list_edit_tools, though it never names a sibling to contrast against.

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?

Explicitly frames the use case: call this to discover valid prompt_style values and style metadata before composing an inference request. It gives clear positive guidance but no exclusions or named alternatives (e.g., user styles via create_user_style/list of user styles).

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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