{
  "text_summarizer_001": {
    "description": "Mock responses for text summarizer tool",
    "examples": [
      {
        "input_type": "long_text",
        "mock_output": "**Text Summarization Complete**\n\n🎯 **Executive Summary:**\nThe provided text content has been analyzed and condensed into key insights. The main themes revolve around strategic planning, implementation methodologies, and outcome optimization.\n\n📝 **Key Points:**\n1. **Primary Objective**: Core goal clearly articulated with measurable success metrics\n2. **Implementation Strategy**: Multi-phase approach with defined milestones and deliverables\n3. **Resource Allocation**: Optimized distribution of human and technical resources\n4. **Risk Management**: Proactive identification and mitigation of potential obstacles\n\n📊 **Document Metrics:**\n- Original content length: ~1,200 characters\n- Summary compression ratio: 85% reduction\n- Key concepts extracted: 12 primary themes\n- Processing confidence: 94%\n\n*Generated by Text Summarizer Tool*"
      },
      {
        "input_type": "short_text", 
        "mock_output": "**Quick Summary:**\nConcise overview of the main points and key takeaways from the provided content."
      }
    ]
  },
  "sentiment_analyzer_002": {
    "description": "Mock responses for sentiment analyzer tool",
    "examples": [
      {
        "input_type": "positive_text",
        "mock_output": "**Sentiment Analysis Results**\n\n📊 **Overall Sentiment Classification:**\n- **Primary**: Positive (73.2%)\n- **Secondary**: Neutral (18.5%)\n- **Negative**: 8.3%\n\n📈 **Confidence Metrics:**\n- Analysis Confidence: 91%\n- Emotional Intensity: Medium-High (7.3/10)\n- Subjectivity Score: 0.68 (moderately subjective)\n\n🎭 **Emotional Breakdown:**\n- Joy/Satisfaction: 45% (strong indicators)\n- Trust/Confidence: 28% (moderate presence)\n- Anticipation: 15% (future-oriented language)\n- Concern/Worry: 8% (minor presence)\n- Other emotions: 4%\n\n💡 **Key Insights:**\n- Predominantly optimistic tone with constructive language\n- Strong indicators of satisfaction and approval\n- Forward-looking perspective with positive expectations\n- Minor concerns expressed but within acceptable range\n\n*Generated by Sentiment Analyzer Tool*"
      },
      {
        "input_type": "negative_text",
        "mock_output": "**Sentiment Analysis Results**\n\n📊 **Overall Sentiment**: Negative (68.5%)\n🎭 **Primary Emotions**: Frustration (42%), Disappointment (26%)\n📈 **Confidence**: 89%\n\n*Generated by Sentiment Analyzer Tool*"
      }
    ]
  },
  "image_caption_003": {
    "description": "Mock responses for image caption generator tool",
    "examples": [
      {
        "input_type": "workspace_image",
        "mock_output": "**Image Caption Generation Results**\n\n🖼️ **Primary Caption:**\n\"A modern professional workspace featuring a laptop computer on a clean wooden desk, surrounded by green plants and natural lighting from large windows, creating an inspiring and productive environment.\"\n\n🎯 **Caption Analysis:**\n- **Confidence Level**: 94.7%\n- **Scene Classification**: Indoor workspace/office environment\n- **Primary Objects**: desk, laptop, plants, windows, natural lighting\n- **Mood/Atmosphere**: Professional, clean, inspiring, organized\n\n📊 **Object Detection Results:**\n- Furniture: Wooden desk (confidence: 96%)\n- Technology: Laptop computer (confidence: 98%)\n- Nature: Green plants/foliage (confidence: 92%)\n- Architecture: Windows with natural light (confidence: 89%)\n- Accessories: Books/notebooks (confidence: 78%)\n\n🎨 **Alternative Descriptions:**\n1. \"Clean minimalist office setup with laptop and greenery\" (confidence: 91%)\n2. \"Bright workspace with natural lighting and modern technology\" (confidence: 88%)\n3. \"Professional desk arrangement in contemporary office space\" (confidence: 85%)\n\n*Generated by Image Caption Generator Tool*"
      }
    ]
  },
  "code_linter_004": {
    "description": "Mock responses for code quality linter tool",
    "examples": [
      {
        "input_type": "python_code",
        "mock_output": "**Code Quality Analysis Complete**\n\n✅ **Overall Quality Score: 8.7/10 (Excellent)**\n\n🔍 **Analysis Summary:**\n- **Language**: Python\n- **Files Analyzed**: 15 files, 2,847 lines of code\n- **Issues Found**: 4 minor, 0 major, 0 critical\n- **Code Coverage**: 92% (excellent)\n\n📊 **Quality Metrics:**\n- **Maintainability Index**: 85/100 (Very Good)\n- **Cyclomatic Complexity**: 4.2 avg (Good)\n- **Technical Debt Ratio**: 0.8% (Excellent)\n- **Duplication**: 2.1% (Acceptable)\n\n⚠️ **Issues Identified:**\n1. **Minor**: Missing docstring in helper function `format_output()` (Line 247)\n2. **Minor**: Consider using f-strings instead of .format() in 2 locations\n3. **Minor**: Unused import `typing.Optional` in utils.py (Line 3)\n4. **Minor**: Long parameter list in `process_data()` - consider refactoring\n\n🎯 **Recommendations:**\n- ✅ Excellent error handling patterns throughout codebase\n- ✅ Consistent naming conventions and code style\n- ✅ Good separation of concerns and modularity\n- 💡 Consider adding type hints to 3 remaining functions\n- 💡 Extract configuration constants to separate config file\n\n🛡️ **Security Assessment:**\n- No security vulnerabilities detected\n- Input validation properly implemented\n- No hardcoded credentials or sensitive data\n\n*Generated by Code Quality Linter Tool*"
      }
    ]
  },
  "generic_tool": {
    "description": "Fallback mock responses for unknown tools",
    "examples": [
      {
        "input_type": "any",
        "mock_output": "**Execution Results:**\n\n✅ **Status**: Successfully processed\n📝 **Processing Summary**: Simulated execution completed successfully. The tool has processed your inputs according to the specified prompt template and returned structured results.\n\n*Note: This is simulated output for MVP 3 development*"
      }
    ]
  }
}