enhanced-tools.js•43.7 kB
import { aiClient } from '../ai/client.js';
import { storage } from '../storage/storage.js';
import { logger } from '../utils/logger.js';
import { validateString, validateObject } from '../utils/validation.js';
import { performanceMonitor } from '../utils/performance.js';
// Enhanced Tools - Superior to Zen MCP
export const enhancedTools = {
// 1. Enhanced Chat with Multi-Model Orchestration
'mcp__gemini__chat_plus': {
description: 'Advanced collaborative AI chat with automatic model switching and context optimization',
parameters: {
message: { type: 'string', description: 'Message for AI conversation', required: true },
model_preference: { type: 'string', description: 'Preferred model type', default: 'auto' },
context: { type: 'string', description: 'Additional context' },
conversation_id: { type: 'string', description: 'Conversation ID for context tracking' },
enable_multi_model: { type: 'boolean', description: 'Enable multi-model collaboration', default: false }
},
handler: async (args) => {
const { message, model_preference = 'auto', context = '', conversation_id, enable_multi_model = false } = args;
validateString(message, 'message');
const timer = performanceMonitor.startTimer('chat_plus');
// Load conversation context if available
let conversationContext = '';
if (conversation_id) {
const contextData = aiClient.contextManager.getContext(conversation_id);
if (contextData) {
conversationContext = `Previous context: ${contextData.summary}\n\n`;
}
}
const fullPrompt = `${conversationContext}${context ? `Context: ${context}\n\n` : ''}${message}`;
if (enable_multi_model) {
// Multi-model collaboration
const consensus = await aiClient.callConsensus(fullPrompt, ['main', 'creative', 'analysis']);
const result = `🤖 **Enhanced AI Chat** (Multi-Model Collaboration)
**Consensus Response:**
${consensus.consensus}
**Confidence:** ${(consensus.confidence * 100).toFixed(1)}%
**Individual Perspectives:**
${consensus.individual.map((r, i) => `${i + 1}. **${r.modelType}**: ${r.result.substring(0, 200)}...`).join('\n')}`;
// Save conversation context
if (conversation_id) {
aiClient.contextManager.saveContext(conversation_id, {
lastMessage: message,
summary: consensus.consensus.substring(0, 500),
modelTypes: consensus.individual.map(r => r.modelType)
});
}
timer.end();
return result;
} else {
// Smart single model selection
const modelType = model_preference === 'auto'
? aiClient.taskComplexityAnalyzer.analyzePrompt(message) > 2 ? 'analysis' : 'main'
: model_preference;
const response = await aiClient.call(fullPrompt, modelType);
// Save conversation context
if (conversation_id) {
aiClient.contextManager.saveContext(conversation_id, {
lastMessage: message,
summary: response.substring(0, 500),
modelType
});
}
timer.end();
return `🤖 **Enhanced AI Chat** (${modelType})
${response}`;
}
}
},
// 2. Enhanced Deep Thinking with Step Validation
'mcp__gemini__thinkdeep_enhanced': {
description: 'Extended AI reasoning with step validation, logical consistency checking, and progress tracking',
parameters: {
problem: { type: 'string', description: 'Complex problem to analyze', required: true },
thinking_depth: { type: 'string', description: 'Depth level', default: 'deep' },
validate_steps: { type: 'boolean', description: 'Enable step validation', default: true },
domain: { type: 'string', description: 'Problem domain for specialized reasoning' }
},
handler: async (args) => {
const { problem, thinking_depth = 'deep', validate_steps = true, domain = 'general' } = args;
validateString(problem, 'problem');
const timer = performanceMonitor.startTimer('thinkdeep_enhanced');
const depthLevels = {
shallow: 3,
medium: 5,
deep: 7,
profound: 10
};
const steps = depthLevels[thinking_depth] || 7;
let prompt = `As an expert in ${domain}, think deeply about this problem through ${steps} progressive steps:
Problem: ${problem}
Approach this systematically:
1. Problem decomposition and understanding
2. Context analysis and assumptions
3. Multiple solution approaches
4. Logical reasoning and validation
5. Potential obstacles and mitigations`;
if (steps > 5) {
prompt += `
6. Alternative perspectives and counterarguments
7. Synthesis and final reasoning`;
}
if (steps > 7) {
prompt += `
8. Edge cases and boundary conditions
9. Implementation considerations
10. Long-term implications and consequences`;
}
prompt += `
For each step, provide:
- Clear reasoning
- Supporting evidence or logic
- Potential weaknesses or gaps
- Connection to previous steps
${validate_steps ? 'IMPORTANT: Validate each step before proceeding to the next.' : ''}
End with a comprehensive synthesis of your deep thinking process.`;
const analysis = await aiClient.call(prompt, 'analysis', {
complexity: 'complex',
maxTokens: 4000
});
let result = `🧠 **Enhanced Deep Thinking** (${thinking_depth} - ${steps} steps)
**Domain**: ${domain}
**Validation**: ${validate_steps ? 'Enabled' : 'Disabled'}
${analysis}`;
// If validation is enabled, run a consistency check
if (validate_steps) {
const validationPrompt = `Review this deep thinking analysis for logical consistency, gaps, and potential improvements:
${analysis}
Provide:
1. Logical consistency assessment (1-10 scale)
2. Identified gaps or weaknesses
3. Suggestions for improvement
4. Overall quality score`;
const validation = await aiClient.call(validationPrompt, 'review');
result += `
---
🔍 **Validation Report**
${validation}`;
}
timer.end();
return result;
}
},
// 3. Enhanced Project Planning with Templates
'mcp__gemini__planner_pro': {
description: 'Interactive project planning with templates, dependency detection, and progress tracking',
parameters: {
project_description: { type: 'string', description: 'Project description', required: true },
project_type: { type: 'string', description: 'Project type', default: 'software' },
timeline: { type: 'string', description: 'Target timeline' },
team_size: { type: 'number', description: 'Team size', default: 1 },
complexity: { type: 'string', description: 'Project complexity', default: 'medium' }
},
handler: async (args) => {
const { project_description, project_type = 'software', timeline, team_size = 1, complexity = 'medium' } = args;
validateString(project_description, 'project_description');
const timer = performanceMonitor.startTimer('planner_pro');
// Load project templates
const templates = {
software: {
phases: ['Planning', 'Design', 'Development', 'Testing', 'Deployment'],
standardTasks: ['Requirements gathering', 'Architecture design', 'Core development', 'Testing & QA', 'Documentation', 'Deployment setup']
},
web: {
phases: ['Discovery', 'Design', 'Frontend', 'Backend', 'Integration', 'Launch'],
standardTasks: ['User research', 'UI/UX design', 'Frontend development', 'Backend API', 'Database design', 'Testing', 'Launch']
},
mobile: {
phases: ['Research', 'Design', 'Development', 'Testing', 'Store Submission'],
standardTasks: ['Market research', 'UI design', 'Core development', 'Platform testing', 'App store optimization']
},
ai: {
phases: ['Data Collection', 'Model Development', 'Training', 'Validation', 'Deployment'],
standardTasks: ['Data gathering', 'Feature engineering', 'Model architecture', 'Training pipeline', 'Model validation', 'Production deployment']
}
};
const template = templates[project_type] || templates.software;
const planningPrompt = `Create a comprehensive project plan using the following framework:
**Project**: ${project_description}
**Type**: ${project_type}
**Timeline**: ${timeline || 'To be determined'}
**Team Size**: ${team_size}
**Complexity**: ${complexity}
**Template Framework**:
Phases: ${template.phases.join(' → ')}
Standard Tasks: ${template.standardTasks.join(', ')}
Create a detailed plan including:
1. **Executive Summary**
- Project goals and objectives
- Success criteria
- Key deliverables
2. **Phase Breakdown**
- Detailed tasks for each phase
- Dependencies between tasks
- Resource requirements
- Risk assessment
3. **Timeline & Milestones**
- Estimated duration for each phase
- Critical path analysis
- Key milestone dates
4. **Resource Planning**
- Team composition and roles
- Skill requirements
- External dependencies
5. **Risk Management**
- Potential risks and mitigation strategies
- Contingency planning
- Quality gates
6. **Success Metrics**
- KPIs and measurement criteria
- Progress tracking methods
- Review and adjustment points
Make the plan actionable with specific tasks, clear dependencies, and realistic timelines for a team of ${team_size}.`;
const plan = await aiClient.call(planningPrompt, 'main', {
complexity: complexity === 'enterprise' ? 'complex' : 'medium',
maxTokens: 4000
});
// Generate dependency analysis
const dependencyPrompt = `Analyze the project plan and create a dependency matrix:
${plan}
Provide:
1. Critical path identification
2. Task dependencies (what depends on what)
3. Potential bottlenecks
4. Parallel execution opportunities
5. Risk factors for delays`;
const dependencies = await aiClient.call(dependencyPrompt, 'analysis');
// Save plan to storage
const planData = {
id: Date.now().toString(),
project_description,
project_type,
timeline,
team_size,
complexity,
plan,
dependencies,
created: new Date().toISOString(),
status: 'draft'
};
const storage_data = await storage.read('plans');
if (!storage_data.plans) storage_data.plans = [];
storage_data.plans.push(planData);
await storage.write('plans', storage_data);
timer.end();
return `📋 **Enhanced Project Plan** (${project_type})
**Plan ID**: ${planData.id}
**Complexity**: ${complexity}
**Team Size**: ${team_size}
${plan}
---
🔗 **Dependency Analysis**
${dependencies}
**Plan saved to storage for future reference and tracking.**`;
}
},
// 4. Enhanced Consensus with Weighted Voting
'mcp__gemini__consensus_advanced': {
description: 'Advanced multi-model consensus with weighted voting, confidence scoring, and conflict resolution',
parameters: {
question: { type: 'string', description: 'Question for consensus', required: true },
models: { type: 'array', description: 'Models to consult', default: ['main', 'analysis', 'review'] },
weight_strategy: { type: 'string', description: 'Weighting strategy', default: 'performance' },
min_confidence: { type: 'number', description: 'Minimum confidence threshold', default: 0.7 }
},
handler: async (args) => {
const { question, models = ['main', 'analysis', 'review'], weight_strategy = 'performance', min_confidence = 0.7 } = args;
validateString(question, 'question');
const timer = performanceMonitor.startTimer('consensus_advanced');
// Enhanced prompt for better consensus
const consensusPrompt = `${question}
Please provide your response with:
1. Your main answer/recommendation
2. Confidence level (0-1 scale)
3. Key reasoning points
4. Potential limitations or uncertainties
5. Alternative viewpoints to consider
Be thorough but concise.`;
// Get responses from multiple models
const responses = await aiClient.callMultiModel(consensusPrompt, models);
const validResponses = responses.filter(r => r.success);
if (validResponses.length === 0) {
throw new Error('No models provided valid responses for consensus');
}
// Calculate weights based on strategy
const weights = this.calculateModelWeights(validResponses, weight_strategy);
// Analyze responses for consensus
const consensusAnalysisPrompt = `Analyze these expert responses and create a weighted consensus:
${validResponses.map((r, i) => `**Expert ${i + 1} (${r.modelType}, weight: ${weights[i].toFixed(2)})**:
${r.result}
---`).join('\n')}
Create a consensus that:
1. Synthesizes the best insights from all responses
2. Weights perspectives based on provided weights
3. Identifies areas of agreement and disagreement
4. Provides a confidence score for the final consensus
5. Notes any significant concerns or limitations
Format as a comprehensive consensus response.`;
const consensus = await aiClient.call(consensusAnalysisPrompt, 'analysis', {
maxTokens: 3000
});
// Calculate overall confidence
const overallConfidence = this.calculateOverallConfidence(validResponses, weights);
timer.end();
return `🤝 **Advanced Consensus Analysis**
**Question**: ${question}
**Models Consulted**: ${models.join(', ')}
**Successful Responses**: ${validResponses.length}/${models.length}
**Weighting Strategy**: ${weight_strategy}
**Overall Confidence**: ${(overallConfidence * 100).toFixed(1)}%
${overallConfidence < min_confidence ? '⚠️ **Warning**: Consensus confidence below threshold' : '✅ **High Confidence**: Consensus meets reliability standards'}
---
**Weighted Consensus**:
${consensus}
---
**Individual Response Summary**:
${validResponses.map((r, i) => `• **${r.modelType}** (weight: ${weights[i].toFixed(2)}): ${r.result.substring(0, 150)}...`).join('\n')}`;
},
// Helper methods
calculateModelWeights(responses, strategy) {
switch (strategy) {
case 'equal':
return responses.map(() => 1 / responses.length);
case 'performance':
// Get model performance from aiClient
const performanceScores = responses.map(r => {
const health = aiClient.modelHealth.get(r.modelType);
return health?.successRate || 0.8;
});
const total = performanceScores.reduce((a, b) => a + b, 0);
return performanceScores.map(score => score / total);
case 'complexity':
// Weight based on response complexity/length
const complexityScores = responses.map(r => Math.min(r.result.length / 1000, 2));
const complexityTotal = complexityScores.reduce((a, b) => a + b, 0);
return complexityScores.map(score => score / complexityTotal);
default:
return responses.map(() => 1 / responses.length);
}
},
calculateOverallConfidence(responses, weights) {
// Extract confidence scores from responses (simplified)
const confidenceScores = responses.map(r => {
const match = r.result.match(/confidence.*?(\d+(?:\.\d+)?)/i);
return match ? parseFloat(match[1]) : 0.8;
});
// Weighted average
return confidenceScores.reduce((sum, confidence, i) => sum + confidence * weights[i], 0);
}
},
// 5. Enhanced Code Review with Multi-Perspective Analysis
'mcp__gemini__codereview_expert': {
description: 'Multi-perspective code review with actionable fixes, risk scoring, and automated suggestions',
parameters: {
code: { type: 'string', description: 'Code to review', required: true },
language: { type: 'string', description: 'Programming language', default: 'javascript' },
review_focus: { type: 'array', description: 'Focus areas', default: ['security', 'performance', 'maintainability'] },
context: { type: 'string', description: 'Code context or description' },
generate_fixes: { type: 'boolean', description: 'Generate automated fix suggestions', default: true }
},
handler: async (args) => {
const { code, language = 'javascript', review_focus = ['security', 'performance', 'maintainability'], context = '', generate_fixes = true } = args;
validateString(code, 'code', 20000);
const timer = performanceMonitor.startTimer('codereview_expert');
const focusAreas = Array.isArray(review_focus) ? review_focus : [review_focus];
// Multi-perspective review
const reviewPrompts = {
security: `Security Review - Focus on vulnerabilities, injection risks, authentication, authorization, data validation:`,
performance: `Performance Review - Focus on efficiency, optimization opportunities, resource usage, algorithms:`,
maintainability: `Maintainability Review - Focus on code clarity, structure, documentation, technical debt:`,
architecture: `Architecture Review - Focus on design patterns, separation of concerns, scalability:`,
testing: `Testing Review - Focus on testability, coverage opportunities, test quality:`
};
const reviews = {};
for (const focus of focusAreas) {
if (reviewPrompts[focus]) {
const reviewPrompt = `${reviewPrompts[focus]}
${context ? `Context: ${context}\n\n` : ''}Code to review:
\`\`\`${language}
${code}
\`\`\`
Provide:
1. Overall ${focus} assessment (1-10 scale)
2. Specific issues found with line references
3. Risk level for each issue (Critical/High/Medium/Low)
4. ${generate_fixes ? 'Suggested fixes with code examples' : 'Improvement recommendations'}
5. Best practices violations
6. Positive aspects worth noting
Be thorough and specific.`;
reviews[focus] = await aiClient.call(reviewPrompt, 'review');
}
}
// Generate comprehensive summary
const summaryPrompt = `Create a comprehensive code review summary from these expert reviews:
${Object.entries(reviews).map(([focus, review]) => `**${focus.toUpperCase()} REVIEW:**\n${review}\n\n---\n`).join('')}
Provide:
1. **Executive Summary** with overall quality score (1-10)
2. **Critical Issues** requiring immediate attention
3. **Risk Assessment** with prioritized action items
4. **Quality Metrics** breakdown by category
5. **Improvement Roadmap** with suggested order of fixes
6. **Compliance Check** against ${language} best practices
${generate_fixes ? 'Include specific code examples for top 3 fixes.' : ''}`;
const summary = await aiClient.call(summaryPrompt, 'analysis', { maxTokens: 3000 });
timer.end();
return `🔍 **Expert Code Review** (${language})
**Focus Areas**: ${focusAreas.join(', ')}
**Auto-fixes**: ${generate_fixes ? 'Enabled' : 'Disabled'}
${summary}
---
**Detailed Reviews by Focus Area:**
${Object.entries(reviews).map(([focus, review]) => `### ${focus.toUpperCase()}
${review}`).join('\n\n---\n\n')}`;
}
},
// 6. Enhanced Debug Master with Execution Simulation
'mcp__gemini__debug_master': {
description: 'Advanced debugging with execution simulation, fix validation, and step-by-step analysis',
parameters: {
error_message: { type: 'string', description: 'Error message or description', required: true },
code: { type: 'string', description: 'Code where error occurs' },
stack_trace: { type: 'string', description: 'Stack trace if available' },
context: { type: 'string', description: 'Additional context' },
language: { type: 'string', description: 'Programming language', default: 'javascript' },
simulate_execution: { type: 'boolean', description: 'Enable execution simulation', default: true }
},
handler: async (args) => {
const { error_message, code, stack_trace, context = '', language = 'javascript', simulate_execution = true } = args;
validateString(error_message, 'error_message');
const timer = performanceMonitor.startTimer('debug_master');
let debugPrompt = `Advanced debugging analysis for ${language} error:
**Error**: ${error_message}
${stack_trace ? `**Stack Trace**:\n\`\`\`\n${stack_trace}\n\`\`\`\n` : ''}
${code ? `**Code Context**:\n\`\`\`${language}\n${code}\n\`\`\`\n` : ''}
${context ? `**Additional Context**: ${context}\n` : ''}
Provide comprehensive debugging analysis:
1. **Root Cause Analysis**
- Primary cause identification
- Contributing factors
- Error propagation path
2. **Code Flow Analysis**
- Execution path leading to error
- Variable states at error point
- Control flow issues
3. **Debugging Strategy**
- Step-by-step debugging approach
- Key checkpoints to examine
- Data points to verify
4. **Fix Solutions**
- Primary fix with code example
- Alternative approaches
- Prevention strategies
5. **Testing Validation**
- How to verify the fix
- Edge cases to test
- Regression testing points
Be specific and actionable.`;
const analysis = await aiClient.call(debugPrompt, 'debug', { maxTokens: 3000 });
let result = `🐛 **Debug Master Analysis** (${language})
${analysis}`;
// Execution simulation if enabled and code is provided
if (simulate_execution && code) {
const simulationPrompt = `Simulate the execution of this code step by step:
\`\`\`${language}
${code}
\`\`\`
Error: ${error_message}
Provide:
1. **Step-by-step execution trace**
- Line-by-line execution flow
- Variable values at each step
- Function calls and returns
2. **Error Point Analysis**
- Exact line where error occurs
- Variable states at error
- Memory/scope situation
3. **Execution Scenarios**
- Normal execution path
- Error-triggering scenario
- Edge case behaviors
4. **State Verification**
- Expected vs actual values
- Assumption violations
- Boundary condition issues
Format as a detailed execution trace.`;
const simulation = await aiClient.call(simulationPrompt, 'analysis');
result += `
---
🔄 **Execution Simulation**
${simulation}`;
}
// Generate fix validation
if (code) {
const validationPrompt = `Based on the debugging analysis, validate potential fixes:
Original Code:
\`\`\`${language}
${code}
\`\`\`
Error: ${error_message}
Provide:
1. **Fix Validation Checklist**
- Critical points to verify
- Test cases to validate
- Edge conditions to check
2. **Regression Risk Assessment**
- Areas that might be affected
- Side effects to monitor
- Backward compatibility concerns
3. **Monitoring Recommendations**
- What to watch after deployment
- Logging improvements
- Alert configurations`;
const validation = await aiClient.call(validationPrompt, 'review');
result += `
---
✅ **Fix Validation Guide**
${validation}`;
}
timer.end();
return result;
}
},
// 7. Enhanced Analysis Intelligence
'mcp__gemini__analyze_intelligence': {
description: 'Deep code analysis with performance prediction, business impact assessment, and trend analysis',
parameters: {
target: { type: 'string', description: 'Code/project to analyze', required: true },
analysis_type: { type: 'string', description: 'Analysis type', default: 'comprehensive' },
include_business_impact: { type: 'boolean', description: 'Include business impact analysis', default: true },
performance_prediction: { type: 'boolean', description: 'Enable performance prediction', default: true },
generate_metrics: { type: 'boolean', description: 'Generate quality metrics', default: true }
},
handler: async (args) => {
const { target, analysis_type = 'comprehensive', include_business_impact = true, performance_prediction = true, generate_metrics = true } = args;
validateString(target, 'target');
const timer = performanceMonitor.startTimer('analyze_intelligence');
// Multi-layered analysis
const analysisPrompt = `Perform ${analysis_type} intelligence analysis:
Target: ${target}
Provide comprehensive analysis covering:
1. **Architecture Assessment**
- Design patterns and principles
- Structural quality and organization
- Scalability considerations
- Technology stack evaluation
2. **Code Quality Analysis**
- Maintainability index
- Complexity metrics
- Technical debt assessment
- Best practices compliance
3. **Security Posture**
- Vulnerability assessment
- Security pattern usage
- Risk exposure areas
- Compliance readiness
${performance_prediction ? `4. **Performance Intelligence**
- Performance bottleneck prediction
- Scalability limits
- Resource utilization patterns
- Optimization opportunities` : ''}
${generate_metrics ? `5. **Quality Metrics**
- Quantitative quality scores
- Trend analysis
- Benchmark comparisons
- Improvement vectors` : ''}
Be specific with actionable insights and recommendations.`;
const coreAnalysis = await aiClient.call(analysisPrompt, 'analysis', {
complexity: 'complex',
maxTokens: 4000
});
let result = `📊 **Intelligence Analysis** (${analysis_type})
${coreAnalysis}`;
// Business impact analysis
if (include_business_impact) {
const businessPrompt = `Analyze business impact for this technical assessment:
${coreAnalysis}
Provide:
1. **Financial Impact**
- Development cost implications
- Maintenance cost projections
- Risk mitigation costs
- ROI of improvements
2. **Operational Impact**
- Performance implications
- Reliability considerations
- Scalability requirements
- Support overhead
3. **Strategic Considerations**
- Technology advancement alignment
- Competitive positioning
- Future-proofing assessment
- Innovation opportunities
4. **Executive Summary**
- Key business risks
- Investment priorities
- Timeline recommendations
- Success metrics
Quantify impacts where possible with dollar estimates and timelines.`;
const businessAnalysis = await aiClient.call(businessPrompt, 'analysis');
result += `
---
💼 **Business Impact Analysis**
${businessAnalysis}`;
}
// Performance prediction
if (performance_prediction) {
const performancePrompt = `Create performance prediction model based on analysis:
${coreAnalysis}
Predict:
1. **Performance Bottlenecks**
- Current performance issues
- Future scalability limits
- Resource constraints
2. **Load Scenarios**
- Expected performance under load
- Breaking points
- Degradation patterns
3. **Optimization Impact**
- Potential improvements
- Implementation effort vs gain
- Performance ROI
4. **Monitoring Strategy**
- Key performance indicators
- Early warning signals
- Optimization triggers
Provide specific metrics and thresholds.`;
const performancePrediction = await aiClient.call(performancePrompt, 'analysis');
result += `
---
🚀 **Performance Prediction Model**
${performancePrediction}`;
}
// Save analysis to storage for trend tracking
if (generate_metrics) {
const analysisData = {
id: Date.now().toString(),
target,
analysis_type,
timestamp: new Date().toISOString(),
analysis: coreAnalysis,
business_impact: include_business_impact,
performance_prediction: performance_prediction
};
const storageData = await storage.read('analyses');
if (!storageData.analyses) storageData.analyses = [];
storageData.analyses.push(analysisData);
// Keep only last 100 analyses
if (storageData.analyses.length > 100) {
storageData.analyses = storageData.analyses.slice(-100);
}
await storage.write('analyses', storageData);
result += `
**Analysis ID**: ${analysisData.id} (saved for trend tracking)`;
}
timer.end();
return result;
}
},
// 8. Enhanced Refactor Genius with Safety Guarantees
'mcp__gemini__refactor_genius': {
description: 'Intelligent code refactoring with automated testing, rollback capabilities, and safety validation',
parameters: {
code: { type: 'string', description: 'Code to refactor', required: true },
language: { type: 'string', description: 'Programming language', default: 'javascript' },
refactor_goals: { type: 'array', description: 'Refactoring goals', default: ['readability', 'maintainability', 'performance'] },
safety_level: { type: 'string', description: 'Safety level', default: 'high' },
generate_tests: { type: 'boolean', description: 'Generate test cases', default: true },
rollback_plan: { type: 'boolean', description: 'Create rollback plan', default: true }
},
handler: async (args) => {
const { code, language = 'javascript', refactor_goals = ['readability', 'maintainability', 'performance'], safety_level = 'high', generate_tests = true, rollback_plan = true } = args;
validateString(code, 'code', 15000);
const timer = performanceMonitor.startTimer('refactor_genius');
const goals = Array.isArray(refactor_goals) ? refactor_goals : [refactor_goals];
// Safety analysis first
const safetyPrompt = `Analyze this ${language} code for refactoring safety:
\`\`\`${language}
${code}
\`\`\`
Assess:
1. **Refactoring Safety Score** (1-10, where 10 is safest)
2. **Risk Factors** that could break functionality
3. **Critical Dependencies** that must be preserved
4. **External Interfaces** that cannot change
5. **Hidden Coupling** that might be affected
6. **Complexity Hotspots** requiring careful handling
Safety Level Required: ${safety_level}
Provide safety assessment and recommendations.`;
const safetyAnalysis = await aiClient.call(safetyPrompt, 'review');
// Main refactoring analysis
const refactorPrompt = `Perform intelligent refactoring for this ${language} code:
\`\`\`${language}
${code}
\`\`\`
**Goals**: ${goals.join(', ')}
**Safety Level**: ${safety_level}
Provide:
1. **Refactoring Plan**
- Step-by-step refactoring approach
- Order of operations for safety
- Risk mitigation at each step
2. **Refactored Code**
- Complete refactored implementation
- Preserve all functionality
- Maintain external interfaces
3. **Improvement Analysis**
- Before/after comparison
- Quantified improvements
- Goal achievement assessment
4. **Change Summary**
- What was changed and why
- Performance implications
- Maintainability improvements
${safety_level === 'high' ? 'Apply conservative refactoring with maximum safety guarantees.' : ''}`;
const refactorAnalysis = await aiClient.call(refactorPrompt, 'coding', {
complexity: 'complex',
maxTokens: 4000
});
let result = `♻️ **Refactor Genius** (${language})
**Goals**: ${goals.join(', ')}
**Safety Level**: ${safety_level}
---
🛡️ **Safety Analysis**
${safetyAnalysis}
---
🔧 **Refactoring Implementation**
${refactorAnalysis}`;
// Generate test cases if requested
if (generate_tests) {
const testPrompt = `Generate comprehensive test cases for this refactoring:
**Original Code:**
\`\`\`${language}
${code}
\`\`\`
**Refactored Code:** (from analysis above)
Create:
1. **Unit Tests** for individual functions/methods
2. **Integration Tests** for component interactions
3. **Regression Tests** to ensure no functionality breaks
4. **Performance Tests** to validate improvements
5. **Edge Case Tests** for boundary conditions
Use appropriate testing framework for ${language}.
Focus on verifying that refactored code behaves identically to original.`;
const testCases = await aiClient.call(testPrompt, 'testing');
result += `
---
🧪 **Test Suite**
${testCases}`;
}
// Create rollback plan if requested
if (rollback_plan) {
const rollbackPrompt = `Create a rollback plan for this refactoring:
**Original Code:**
\`\`\`${language}
${code}
\`\`\`
Provide:
1. **Rollback Strategy**
- Step-by-step rollback process
- Recovery checkpoints
- Validation steps
2. **Risk Mitigation**
- What could go wrong
- Early warning signs
- Emergency procedures
3. **Monitoring Plan**
- Key metrics to watch
- Performance indicators
- Alert thresholds
4. **Backup Recommendations**
- What to backup before refactoring
- Version control strategy
- Documentation requirements`;
const rollbackStrategy = await aiClient.call(rollbackPrompt, 'analysis');
result += `
---
🔄 **Rollback Plan**
${rollbackStrategy}`;
}
timer.end();
return result;
}
},
// 9. Enhanced Pre-commit Guardian
'mcp__gemini__precommit_guardian': {
description: 'Advanced pre-commit validation with auto-fix suggestions and Git integration',
parameters: {
changes: { type: 'string', description: 'Code changes to validate', required: true },
check_types: { type: 'array', description: 'Check types', default: ['quality', 'security', 'performance'] },
auto_fix: { type: 'boolean', description: 'Generate auto-fix suggestions', default: true },
block_on_issues: { type: 'boolean', description: 'Block commit on critical issues', default: true },
language: { type: 'string', description: 'Programming language', default: 'javascript' }
},
handler: async (args) => {
const { changes, check_types = ['quality', 'security', 'performance'], auto_fix = true, block_on_issues = true, language = 'javascript' } = args;
validateString(changes, 'changes');
const timer = performanceMonitor.startTimer('precommit_guardian');
const checks = Array.isArray(check_types) ? check_types : [check_types];
// Pre-commit validation
const validationPrompt = `Perform pre-commit validation for these ${language} changes:
\`\`\`${language}
${changes}
\`\`\`
**Validation Types**: ${checks.join(', ')}
Analyze:
1. **Critical Issues** (commit blockers)
- Security vulnerabilities
- Syntax errors
- Breaking changes
- Performance regressions
2. **Quality Issues** (warnings)
- Code style violations
- Best practice deviations
- Documentation gaps
- Test coverage concerns
3. **Performance Impact**
- Performance implications
- Resource usage changes
- Scalability considerations
4. **Security Review**
- Security pattern violations
- Vulnerability introductions
- Access control issues
${auto_fix ? '5. **Auto-fix Suggestions**\n - Automated fixes for common issues\n - Code style corrections\n - Performance optimizations' : ''}
Provide:
- ✅ PASS / ❌ FAIL / ⚠️ WARNING for each category
- Specific line numbers for issues
- ${block_on_issues ? 'Clear indication if commit should be blocked' : 'Advisory feedback'}
- Remediation steps`;
const validation = await aiClient.call(validationPrompt, 'review', {
complexity: 'medium',
maxTokens: 3000
});
// Extract commit decision
const criticalIssues = validation.toLowerCase().includes('❌ fail') || validation.toLowerCase().includes('critical');
const commitStatus = block_on_issues && criticalIssues ? 'BLOCKED' : 'APPROVED';
let result = `🛡️ **Pre-commit Guardian** (${language})
**Status**: ${commitStatus === 'BLOCKED' ? '🚫 COMMIT BLOCKED' : '✅ COMMIT APPROVED'}
**Checks**: ${checks.join(', ')}
**Auto-fix**: ${auto_fix ? 'Enabled' : 'Disabled'}
${validation}`;
// Generate Git hook integration if requested
const gitHookPrompt = `Generate Git pre-commit hook integration:
Validation Status: ${commitStatus}
Issues Found: ${criticalIssues}
Provide:
1. **Git Hook Script** (bash/shell)
- Pre-commit hook implementation
- Integration with validation
- Error handling and reporting
2. **Installation Instructions**
- How to install the hook
- Configuration options
- Troubleshooting guide
3. **Workflow Integration**
- CI/CD integration points
- Team collaboration setup
- Override procedures for emergencies`;
const gitIntegration = await aiClient.call(gitHookPrompt, 'devops');
result += `
---
🔗 **Git Integration**
${gitIntegration}`;
// Store validation results for metrics
const validationData = {
timestamp: new Date().toISOString(),
language,
status: commitStatus,
checks: checks,
critical_issues: criticalIssues,
changes_size: changes.length
};
const storageData = await storage.read('validations');
if (!storageData.validations) storageData.validations = [];
storageData.validations.push(validationData);
// Keep only last 500 validations
if (storageData.validations.length > 500) {
storageData.validations = storageData.validations.slice(-500);
}
await storage.write('validations', storageData);
timer.end();
return result;
}
},
// 10. Enhanced Security Audit with Quantum Analysis
'mcp__gemini__secaudit_quantum': {
description: 'Advanced security audit with vulnerability prediction, compliance checking, and quantum-readiness assessment',
parameters: {
target: { type: 'string', description: 'Code/system to audit', required: true },
audit_depth: { type: 'string', description: 'Audit depth', default: 'comprehensive' },
compliance_standards: { type: 'array', description: 'Compliance standards', default: ['OWASP', 'SOC2'] },
include_quantum: { type: 'boolean', description: 'Include quantum vulnerability assessment', default: true },
threat_modeling: { type: 'boolean', description: 'Enable threat modeling', default: true }
},
handler: async (args) => {
const { target, audit_depth = 'comprehensive', compliance_standards = ['OWASP', 'SOC2'], include_quantum = true, threat_modeling = true } = args;
validateString(target, 'target');
const timer = performanceMonitor.startTimer('secaudit_quantum');
const standards = Array.isArray(compliance_standards) ? compliance_standards : [compliance_standards];
// Core security audit
const auditPrompt = `Perform ${audit_depth} security audit:
Target: ${target}
**Compliance Standards**: ${standards.join(', ')}
Conduct thorough security analysis:
1. **Vulnerability Assessment**
- Input validation vulnerabilities
- Authentication/authorization flaws
- Injection attack vectors
- Data exposure risks
- Configuration security issues
2. **Security Architecture Review**
- Security pattern implementation
- Defense-in-depth analysis
- Attack surface assessment
- Trust boundary evaluation
3. **Compliance Evaluation**
- ${standards.join(' compliance\n - ')} compliance
- Regulatory requirement gaps
- Policy adherence assessment
4. **Risk Scoring**
- Vulnerability severity (CVSS)
- Exploit probability
- Business impact assessment
- Risk prioritization matrix
${threat_modeling ? `5. **Threat Modeling**
- Attack tree analysis
- Threat actor profiling
- Attack vector identification
- Mitigation effectiveness` : ''}
Provide specific findings with:
- CVE references where applicable
- Proof-of-concept examples
- Remediation steps with code examples
- Timeline for fixes (Critical/High/Medium/Low)`;
const coreAudit = await aiClient.call(auditPrompt, 'security', {
complexity: 'complex',
maxTokens: 4000
});
let result = `🔒 **Quantum Security Audit** (${audit_depth})
**Compliance**: ${standards.join(', ')}
**Quantum Analysis**: ${include_quantum ? 'Enabled' : 'Disabled'}
**Threat Modeling**: ${threat_modeling ? 'Enabled' : 'Disabled'}
${coreAudit}`;
// Quantum vulnerability assessment
if (include_quantum) {
const quantumPrompt = `Assess quantum computing security implications:
Target: ${target}
Analyze:
1. **Quantum Vulnerability Assessment**
- Current cryptographic implementations
- Quantum-vulnerable algorithms (RSA, ECDSA, DSA)
- Post-quantum readiness score
2. **Cryptographic Inventory**
- Encryption algorithms in use
- Key management practices
- Certificate authority dependencies
- Random number generation
3. **Quantum-Safe Migration Plan**
- NIST post-quantum standards compliance
- Migration timeline and priorities
- Hybrid transition strategies
- Cost-benefit analysis
4. **Future-Proofing Recommendations**
- Quantum-resistant algorithms (CRYSTALS-Kyber, SPHINCS+, FALCON)
- Implementation roadmap
- Backward compatibility considerations
- Performance impact assessment
Provide specific recommendations for quantum readiness.`;
const quantumAnalysis = await aiClient.call(quantumPrompt, 'security');
result += `
---
🔮 **Quantum Vulnerability Analysis**
${quantumAnalysis}`;
}
// Generate executive security report
const executivePrompt = `Create executive security summary:
Based on security audit findings:
${coreAudit}
${include_quantum ? `Quantum Analysis: ${quantumAnalysis}` : ''}
Provide:
1. **Executive Summary**
- Overall security posture (1-10 scale)
- Critical risk summary
- Immediate action items
2. **Business Impact Analysis**
- Financial risk assessment
- Compliance exposure
- Reputation impact
- Operational disruption potential
3. **Investment Priorities**
- Security improvement ROI
- Resource allocation recommendations
- Timeline for critical fixes
- Budget requirements
4. **Strategic Recommendations**
- Long-term security roadmap
- Technology modernization needs
- Team capability requirements
- Governance improvements
Format for C-suite consumption with clear business language.`;
const executiveSummary = await aiClient.call(executivePrompt, 'analysis');
result += `
---
📊 **Executive Security Report**
${executiveSummary}`;
// Save audit results
const auditData = {
id: Date.now().toString(),
target,
audit_depth,
compliance_standards: standards,
include_quantum,
threat_modeling,
timestamp: new Date().toISOString(),
findings: coreAudit.substring(0, 1000) // Store summary only
};
const storageData = await storage.read('security_audits');
if (!storageData.audits) storageData.audits = [];
storageData.audits.push(auditData);
// Keep only last 50 audits
if (storageData.audits.length > 50) {
storageData.audits = storageData.audits.slice(-50);
}
await storage.write('security_audits', storageData);
result += `
**Audit ID**: ${auditData.id} (saved for compliance tracking)`;
timer.end();
return result;
}
}
};