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Tai-DT
by Tai-DT

suggest_improvements

Analyze HTML code to receive AI-powered design improvement suggestions for accessibility, performance, UX, aesthetics, and responsiveness based on your context and target audience.

Instructions

Get AI-powered suggestions for design improvements

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
htmlYesHTML code to analyze
contextNoContext about the design goals
targetAudienceNoTarget audience for the design
focusAreasNoAreas to focus improvements on

Implementation Reference

  • The core handler function that executes the 'suggest_improvements' tool logic, returning a structured content response with placeholder suggestion text.
    export async function suggestImprovements(args: SuggestionOptions) {
      return {
        content: [
          {
            type: 'text',
            text: `Generated suggestions for ${args.context || 'general'}\nFocus: ${args.focus || 'all'}`
          }
        ]
      };
    }
  • Input schema validation for the suggest_improvements tool parameters.
    inputSchema: {
      type: 'object',
      properties: {
        html: {
          type: 'string',
          description: 'HTML code to analyze'
        },
        context: {
          type: 'string',
          description: 'Context about the design goals'
        },
        targetAudience: {
          type: 'string',
          description: 'Target audience for the design'
        },
        focusAreas: {
          type: 'array',
          items: {
            type: 'string',
            enum: ['accessibility', 'performance', 'ux', 'aesthetics', 'responsiveness']
          },
          description: 'Areas to focus improvements on'
        }
      },
      required: ['html']
    }
  • src/index.ts:331-360 (registration)
    Tool registration object added to the TOOLS array, used by listTools endpoint.
    {
      name: 'suggest_improvements',
      description: 'Get AI-powered suggestions for design improvements',
      inputSchema: {
        type: 'object',
        properties: {
          html: {
            type: 'string',
            description: 'HTML code to analyze'
          },
          context: {
            type: 'string',
            description: 'Context about the design goals'
          },
          targetAudience: {
            type: 'string',
            description: 'Target audience for the design'
          },
          focusAreas: {
            type: 'array',
            items: {
              type: 'string',
              enum: ['accessibility', 'performance', 'ux', 'aesthetics', 'responsiveness']
            },
            description: 'Areas to focus improvements on'
          }
        },
        required: ['html']
      }
    },
  • Dispatch handler in the CallToolRequestSchema that routes requests for 'suggest_improvements' to the specific implementation function.
    case 'suggest_improvements':
      return await suggestImprovements(args as unknown as SuggestionOptions);
  • Type definition for SuggestionOptions used in the tool handler signature.
    export interface SuggestionOptions {
      html: string;
      context?: 'landing-page' | 'dashboard' | 'component' | 'form' | 'navigation';
      focus?: 'accessibility' | 'performance' | 'design' | 'ux' | 'seo' | 'all';
      priority?: 'critical' | 'important' | 'nice-to-have' | 'all';
      framework?: 'react' | 'vue' | 'svelte' | 'html';
      targetAudience?: string;
      businessGoals?: string[];
    }
Behavior2/5

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. It mentions 'AI-powered suggestions' but doesn't explain how the tool behaves—e.g., whether it's read-only, requires specific permissions, has rate limits, or what the output format might be. This is a significant gap for a tool that likely involves analysis and recommendations.

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 directly states the tool's purpose without unnecessary words. It's front-loaded and every part earns its place, 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.

Completeness2/5

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

Given the complexity of a tool that analyzes HTML for design improvements, the description is incomplete. With no annotations and no output schema, it fails to cover behavioral aspects like safety, permissions, or return values. The schema handles parameters well, but the overall context for agent decision-making is lacking.

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?

The schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional meaning beyond what's in the schema (e.g., it doesn't clarify how 'context' or 'targetAudience' influence suggestions). Baseline 3 is appropriate when the schema does the heavy lifting.

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 action ('Get AI-powered suggestions') and resource ('design improvements'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'analyze_design' or 'optimize_classes', which might offer similar analysis functions, so it doesn't reach the highest score.

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 like 'analyze_design' or 'optimize_classes'. It lacks context about prerequisites, ideal scenarios, or exclusions, leaving the agent to infer usage from the tool name and parameters alone.

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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