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Zenobia000

VibeCoding System

by Zenobia000

provide-clarification

Generate precise clarification responses to user queries by analyzing question indexes and provided answers, enhancing clarity in AI-driven dialogue systems.

Instructions

Provide a clarification answer

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
answerYes
questionIndexYes

Implementation Reference

  • Core handler logic for the 'provide-clarification' tool: processes user answer for a specific question index, updates the project clarification responses, saves the project state, and returns the next question or completion status.
    async provideClarification(
      questionIndex: number, 
      answer: string
    ): Promise<{ success: boolean; nextQuestion?: string; isComplete: boolean; message: string }> {
        const project = this.loadCurrentProject();
      if (!project) {
            throw new Error('No active project found. Please start clarification first.');
      }
    
      const clarificationResponse: ClarificationResponse = {
        question: DEFAULT_CLARIFICATION_QUESTIONS[questionIndex],
        answer,
        timestamp: new Date()
      };
    
        if (!project.clarificationResponses) {
            project.clarificationResponses = [];
        }
      project.clarificationResponses.push(clarificationResponse);
      project.updatedAt = new Date();
        this.saveCurrentProject(project);
    
      const nextIndex = questionIndex + 1;
      if (nextIndex >= DEFAULT_CLARIFICATION_QUESTIONS.length) {
        return {
          success: true,
          isComplete: true,
                message: `✅ Project clarification complete! You can now generate the PRD.`
        };
      }
    
      return {
        success: true,
            nextQuestion: DEFAULT_CLARIFICATION_QUESTIONS[nextIndex],
        isComplete: false,
            message: `✅ Answer recorded. Next question:`
        };
    }
  • MCP CallToolRequestSchema handler case for 'provide-clarification': validates input arguments using Zod, invokes the core provideClarification method, and formats the MCP response content.
    case 'provide-clarification': {
      const { questionIndex, answer } = z.object({
        questionIndex: z.number(),
        answer: z.string()
      }).parse(args);
      const result = await contextManager.provideClarification(questionIndex, answer);
      let text = result.message;
      if(result.nextQuestion) {
          text += `\n\nNext question: ${result.nextQuestion}`;
      }
      return { content: [{ type: 'text', text }] };
    }
  • Tool registration in ListToolsRequestSchema response: defines name, description, and input schema for 'provide-clarification'.
    {
      name: 'provide-clarification',
      description: 'Provide a clarification answer',
      inputSchema: {
        type: 'object',
        properties: {
          questionIndex: { type: 'number' },
          answer: { type: 'string' }
        },
        required: ['questionIndex', 'answer']
      }
    },
  • Input schema definition for the 'provide-clarification' tool, specifying questionIndex (number) and answer (string) as required properties.
    {
      name: 'provide-clarification',
      description: 'Provide a clarification answer',
      inputSchema: {
        type: 'object',
        properties: {
          questionIndex: { type: 'number' },
          answer: { type: 'string' }
        },
        required: ['questionIndex', 'answer']
      }
    },
Behavior1/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure but fails completely. It doesn't indicate whether this is a read or write operation, what permissions might be required, whether it has side effects, what format the clarification takes, or how it interacts with the system. The single phrase offers no behavioral insight beyond the basic action implied by the name.

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 maximally concise - a single three-word phrase with no wasted words. While this conciseness comes at the expense of completeness, the description is perfectly structured as a minimal statement of function without unnecessary elaboration or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity implied by having two required parameters, no annotations, no output schema, and sibling tools that suggest this is part of a larger workflow, the description is completely inadequate. It doesn't explain what a 'clarification answer' is, how it relates to the parameters, what the tool actually does with the input, or what the expected outcome is. This leaves the agent with insufficient information to use the tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage and two required parameters ('answer' and 'questionIndex'), the description provides zero information about what these parameters mean or how they should be used. The description doesn't mention parameters at all, leaving the agent with no semantic understanding of what 'answer' should contain or what 'questionIndex' refers to in the context of providing clarification.

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

Purpose2/5

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

The description 'Provide a clarification answer' is a tautology that essentially restates the tool name 'provide-clarification' with minimal additional meaning. It doesn't specify what kind of clarification, for what purpose, or what resource/context it operates on. While it includes the verb 'provide' and object 'clarification answer', this is too vague to understand the tool's specific function.

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

Usage Guidelines1/5

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

The description provides absolutely no guidance about when to use this tool versus alternatives. With sibling tools like 'generate-impl-plan', 'generate-prd', and 'start-clarification' available, there's no indication of how this tool fits into a workflow or when it should be selected over those other options. The description is completely silent on usage context.

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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