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LumabyteCo

Clarifyprompt-MCP

list_categories

Discover available prompt optimization categories with platform counts to select the right format for AI platforms like Midjourney, DALL-E, and ChatGPT.

Instructions

List all available prompt optimization categories with platform counts including custom platforms

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The 'list_categories' tool handler that executes the tool logic. It retrieves the platform registry, maps over CATEGORIES to build category info with platform counts (total, builtin, custom), and returns JSON formatted output.
    server.tool(
      "list_categories",
      "List all available prompt optimization categories with platform counts including custom platforms",
      {},
      async () => {
        const registry = getPlatformRegistry();
        const cats = await Promise.all(CATEGORIES.map(async c => {
          const allPlatforms = await registry.getPlatformsForCategory(c.id);
          return {
            id: c.id,
            label: c.label,
            description: c.description,
            has_platforms: c.hasPlatforms,
            platform_count: allPlatforms.length,
            builtin_count: c.platforms?.length ?? 0,
            custom_count: allPlatforms.filter(p => p.isCustom).length,
            default_platform: c.defaultPlatform ?? null,
            default_mode: c.defaultMode,
          };
        }));
        return { content: [{ type: "text" as const, text: JSON.stringify(cats, null, 2) }] };
      }
    );
  • Empty input schema `{}` for list_categories tool, indicating no parameters required.
    {},
  • PlatformRegistry.getPlatformsForCategory method used by the handler to fetch both built-in and custom platforms for each category.
    async getPlatformsForCategory(categoryId: Category): Promise<PlatformConfig[]> {
      await this.store.ensureLoaded();
      const builtIn = getBuiltInPlatforms(categoryId);
      const custom = this.store.getCustomPlatforms(categoryId).map(entry => ({
        id: entry.id,
        label: entry.label,
        description: entry.description,
        syntaxHints: entry.syntaxHints,
        instructions: entry.instructions,
        instructionsFile: entry.instructionsFile,
        isCustom: true as const,
      }));
      return [...builtIn, ...custom];
    }
  • CategoryConfig interface defining the structure of category data returned by list_categories, including id, label, description, platforms, defaultPlatform, defaultMode, and hasPlatforms.
    export interface CategoryConfig {
      id: Category;
      label: string;
      description: string;
      platforms?: PlatformConfig[];
      defaultPlatform?: string;
      defaultMode: Mode;
      hasPlatforms: boolean;
    }
  • CATEGORIES constant - the source data array containing all 7 category definitions (chat, image, voice, video, music, code, document) that the handler iterates over.
    export const CATEGORIES: CategoryConfig[] = [
      { id: 'chat', label: 'Chat', description: 'General conversation & Q&A', platforms: CHAT_PLATFORMS, defaultPlatform: 'claude', defaultMode: 'detailed', hasPlatforms: true },
      { id: 'image', label: 'Image', description: 'Image generation', platforms: IMAGE_PLATFORMS, defaultPlatform: 'midjourney', defaultMode: 'detailed', hasPlatforms: true },
      { id: 'voice', label: 'Voice', description: 'Voice & speech synthesis', platforms: VOICE_PLATFORMS, defaultPlatform: 'elevenlabs', defaultMode: 'detailed', hasPlatforms: true },
      { id: 'video', label: 'Video', description: 'Video generation', platforms: VIDEO_PLATFORMS, defaultPlatform: 'runway', defaultMode: 'detailed', hasPlatforms: true },
      { id: 'music', label: 'Music', description: 'Music generation', platforms: MUSIC_PLATFORMS, defaultPlatform: 'suno', defaultMode: 'detailed', hasPlatforms: true },
      { id: 'code', label: 'Code', description: 'Programming & development', platforms: CODE_PLATFORMS, defaultPlatform: 'claude', defaultMode: 'detailed', hasPlatforms: true },
      { id: 'document', label: 'Document', description: 'Writing & documents', platforms: DOCUMENT_PLATFORMS, defaultPlatform: 'claude', defaultMode: 'detailed', hasPlatforms: true },
    ];
Behavior3/5

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

No annotations are provided, so the description carries full burden. It describes what data is returned (categories with platform counts including custom platforms) but doesn't disclose behavioral traits like pagination, rate limits, authentication needs, or error conditions. The description is informative but lacks operational details.

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?

Single sentence with zero waste. Front-loaded with the core action ('List all available prompt optimization categories') and includes essential detail ('with platform counts including custom platforms'). Every word earns its place.

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 no annotations, no output schema, and 0 parameters, the description is adequate but has clear gaps. It explains what the tool returns but not the format, structure, or potential limitations. For a read-only list tool, it meets minimum viability but lacks completeness about behavior and output.

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 with 100% schema description coverage. The description doesn't need to compensate for missing parameter documentation, and it appropriately doesn't discuss parameters. Baseline 4 is appropriate for zero-parameter tools.

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 verb 'List' and the resource 'all available prompt optimization categories with platform counts including custom platforms'. It distinguishes from siblings like list_modes (modes vs categories) and list_platforms (platforms vs categories with platform counts).

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?

No explicit guidance on when to use this tool versus alternatives like list_platforms or list_modes. The description implies it's for viewing categories with platform counts, but doesn't specify use cases, 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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