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LumabyteCo

Clarifyprompt-MCP

list_modes

Lists available output modes for prompt optimization, including concise, detailed, structured, and technical formats, to help users select the best format for their AI platform needs.

Instructions

List available output modes for prompt optimization (concise, detailed, structured, step-by-step, bullet-points, technical, simple)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The list_modes tool handler - registered via server.tool() with an inline async function that returns the MODES array as JSON. This is both the registration point and the handler implementation.
    server.tool(
      "list_modes",
      "List available output modes for prompt optimization (concise, detailed, structured, step-by-step, bullet-points, technical, simple)",
      {},
      async () => {
        return { content: [{ type: "text" as const, text: JSON.stringify(MODES, null, 2) }] };
      }
    );
  • The MODES constant definition - an array of mode objects each with id, label, and description. This is the data source returned by the list_modes tool.
    export const MODES: { id: Mode; label: string; description: string }[] = [
      { id: 'concise', label: 'Concise', description: 'Short and to the point' },
      { id: 'detailed', label: 'Detailed', description: 'Comprehensive with examples' },
      { id: 'structured', label: 'Structured', description: 'Organized with clear sections' },
      { id: 'step-by-step', label: 'Step-by-Step', description: 'Sequential instructions' },
      { id: 'bullet-points', label: 'Bullet Points', description: 'List format, scannable' },
      { id: 'technical', label: 'Technical', description: 'Expert-level depth' },
      { id: 'simple', label: 'Simple', description: 'Plain language, easy to understand' },
    ];
  • The Mode type definition - a union type of all valid mode identifiers: 'concise' | 'detailed' | 'structured' | 'step-by-step' | 'bullet-points' | 'technical' | 'simple'
    export type Mode = 'concise' | 'detailed' | 'structured' | 'step-by-step' | 'bullet-points' | 'technical' | 'simple';
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 only lists the modes without explaining what the tool returns (e.g., a list of strings, JSON objects), any rate limits, authentication needs, or side effects. For a tool with zero annotation coverage, this is a significant gap in transparency.

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 purpose and lists the modes without unnecessary words. Every part earns its place by directly informing the tool's function, making it highly concise and well-structured.

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 low complexity (0 parameters, no output schema), the description is minimally adequate by stating what it lists. However, it lacks details on return format, usage context, or behavioral traits, which are gaps despite the simple nature. With no annotations, it should provide more completeness for effective agent use.

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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description adds value by specifying the types of modes (concise, detailed, etc.), which provides semantic context beyond the empty schema, earning a baseline score above 3 for compensating with relevant information.

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 verb 'List' and the resource 'available output modes for prompt optimization', making the purpose explicit. It distinguishes from siblings like 'list_categories' and 'list_platforms' by specifying the type of modes, though it doesn't explicitly contrast with them. However, it lacks a specific verb-resource distinction beyond the general listing action.

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 does not mention prerequisites, context for usage, or comparisons with sibling tools like 'optimize_prompt', leaving the agent without explicit instructions on application scenarios.

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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