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list_styles

Discover available styles, lighting, camera angles, moods, colors, and quality tags to build effective prompts for AI-generated media.

Instructions

List all available styles, lighting, camera angles, moods, colors, and quality tags for prompt building

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handleListStyles function defines the logic for listing available styles, lighting, camera angles, moods, colors, and quality tags by mapping them into a formatted text response.
    export function handleListStyles() {
      const section = (title: string, map: Record<string, string>) =>
        [`${title}:`, ...Object.entries(map).map(([k, v]) => `  ${k} → ${v}`), ""].join(
          "\n"
        );
    
      const text = [
        section("STYLES", styleMappings),
        section("LIGHTING", lightingMappings),
        section("CAMERA", cameraMappings),
        section("MOOD", moodMappings),
        section("COLOR", colorMappings),
        section("QUALITY TAGS", qualityTagMappings),
      ].join("\n");
    
      return { content: [{ type: "text" as const, text }] };
    }
  • The input schema definition for the list_styles tool.
    export const listStylesSchema = z.object({});
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes a read-only listing operation, which is clear, but lacks details on behavioral traits such as rate limits, authentication needs, response format, or any potential side effects. This leaves significant gaps for an agent to understand how to interact with it effectively.

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 a single, efficient sentence that front-loads the key action and resource without any wasted words. It directly conveys the tool's purpose in a structured manner, making it easy to parse and understand quickly.

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 the tool's complexity is low (0 parameters, no output schema), the description is adequate as a basic listing tool. However, without annotations or an output schema, it lacks completeness in detailing behavioral aspects like response format or usage constraints. It meets the minimum viable standard but has clear gaps in providing full context for an agent.

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?

The tool has 0 parameters, and the schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics, and it appropriately doesn't mention any. A baseline score of 4 is given since no parameters exist, and the description doesn't introduce confusion or redundancy.

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?

The description clearly states the tool's purpose with a specific verb ('List') and resource ('all available styles, lighting, camera angles, moods, colors, and quality tags'), making it easy to understand what it does. However, it doesn't explicitly distinguish this tool from its sibling 'list_models', which might also list available resources, so it misses full sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It mentions 'for prompt building', which implies a context, but doesn't specify when to choose this over sibling tools like 'build_prompt' or 'list_models', nor does it outline any prerequisites or exclusions for usage.

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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