Obsidian Elite RAG MCP Server

/** * Personal Brand Manager Tool * Learns and adapts to user's unique style and audience preferences */ import { z } from 'zod'; interface BrandAnalysis { brand_evolution: { current_score: number; growth_trend: number; authenticity_rating: number; consistency_score: number; audience_alignment: number; }; performance_metrics: { average_engagement: number; retention_improvement: number; audience_growth: number; content_quality_trend: number; platform_performance: Record<string, number>; }; style_analysis: { communication_style: string; expertise_areas: string[]; tone_consistency: number; visual_branding: number; content_themes: string[]; }; audience_insights: { primary_demographics: Record<string, number>; engagement_patterns: Record<string, any>; content_preferences: string[]; optimal_posting_times: string[]; platform_affinity: Record<string, number>; }; recommendations: Array<{ category: string; priority: number; action: string; expected_impact: string; timeframe: string; }>; } interface BrandRecommendations { content_strategy: Array<{ recommendation: string; rationale: string; expected_outcome: string; implementation_effort: string; }>; audience_development: Array<{ target_segment: string; approach: string; content_suggestions: string[]; growth_potential: number; }>; platform_optimization: Array<{ platform: string; current_performance: number; optimization_areas: string[]; specific_actions: string[]; }>; competitive_insights: Array<{ insight: string; opportunity: string; differentiation_strategy: string; }>; } const AnalyzeBrandPerformanceArgsSchema = z.object({ user_id: z.string(), time_period: z.enum(['week', 'month', 'quarter', 'year']).default('month'), include_predictions: z.boolean().default(true), competitive_analysis: z.boolean().default(false), }); const GetBrandRecommendationsArgsSchema = z.object({ user_id: z.string(), focus_areas: z.array( z.enum(['content', 'audience', 'platform', 'growth']) ).optional(), }); const LearnFromPerformanceArgsSchema = z.object({ user_id: z.string(), video_id: z.string(), real_metrics: z.object({ views: z.number(), engagement_rate: z.number(), retention_rate: z.number(), shares: z.number(), comments: z.number(), completion_rate: z.number(), }), platform: z.string(), }); export class PersonalBrandManager { /** * Analyze personal brand evolution and performance */ async analyzeBrandPerformance(args: z.infer<typeof AnalyzeBrandPerformanceArgsSchema>) { const { user_id, time_period, include_predictions, competitive_analysis } = args; console.log(`Analyzing brand performance for user: ${user_id}`); console.log(`Time period: ${time_period}, Predictions: ${include_predictions}`); const analysis = await this.generateBrandAnalysis( user_id, time_period, include_predictions, competitive_analysis ); return { content: [ { type: 'text', text: `# 🎨 Personal Brand Analysis Report\n\n` + `**User ID:** ${user_id}\n` + `**Analysis Period:** ${time_period}\n` + `**Report Generated:** ${new Date().toLocaleDateString()}\n\n` + `## 📈 Brand Evolution Score\n\n` + `- **Overall Brand Score:** ${(analysis.brand_evolution.current_score * 100).toFixed(1)}%\n` + `- **Growth Trend:** ${analysis.brand_evolution.growth_trend > 0 ? '📈' : '📉'} ${(analysis.brand_evolution.growth_trend * 100).toFixed(1)}%\n` + `- **Authenticity Rating:** ${(analysis.brand_evolution.authenticity_rating * 100).toFixed(1)}%\n` + `- **Consistency Score:** ${(analysis.brand_evolution.consistency_score * 100).toFixed(1)}%\n` + `- **Audience Alignment:** ${(analysis.brand_evolution.audience_alignment * 100).toFixed(1)}%\n\n` + `## 📊 Performance Metrics\n\n` + `- **Average Engagement:** ${(analysis.performance_metrics.average_engagement * 100).toFixed(1)}%\n` + `- **Retention Improvement:** ${(analysis.performance_metrics.retention_improvement * 100).toFixed(1)}%\n` + `- **Audience Growth:** ${(analysis.performance_metrics.audience_growth * 100).toFixed(1)}%\n` + `- **Content Quality Trend:** ${(analysis.performance_metrics.content_quality_trend * 100).toFixed(1)}%\n\n` + `### Platform Performance\n` + Object.entries(analysis.performance_metrics.platform_performance).map(([platform, score]) => `- **${platform}:** ${(score * 100).toFixed(1)}%\n` ).join('') + `\n## 🎭 Style Analysis\n\n` + `- **Communication Style:** ${analysis.style_analysis.communication_style}\n` + `- **Expertise Areas:** ${analysis.style_analysis.expertise_areas.join(', ')}\n` + `- **Tone Consistency:** ${(analysis.style_analysis.tone_consistency * 100).toFixed(1)}%\n` + `- **Visual Branding:** ${(analysis.style_analysis.visual_branding * 100).toFixed(1)}%\n` + `- **Content Themes:** ${analysis.style_analysis.content_themes.join(', ')}\n\n` + `## 🎯 Top Recommendations\n\n` + analysis.recommendations.slice(0, 5).map(rec => `- **${rec.category} (Priority ${rec.priority}/10):** ${rec.action}\n` + ` 🎯 Impact: ${rec.expected_impact} | ⏱️ Timeframe: ${rec.timeframe}\n` ).join('\n'), }, { type: 'text', text: JSON.stringify(analysis, null, 2), }, ], }; } /** * Get AI-powered brand optimization recommendations */ async getBrandRecommendations(args: z.infer<typeof GetBrandRecommendationsArgsSchema>) { const { user_id, focus_areas } = args; console.log(`Generating brand recommendations for user: ${user_id}`); console.log(`Focus areas: ${focus_areas?.join(', ') || 'all'}`); const recommendations = await this.generateRecommendations(user_id, focus_areas); return { content: [ { type: 'text', text: `# 🚀 Personalized Brand Recommendations\n\n` + `**User ID:** ${user_id}\n` + `**Focus Areas:** ${focus_areas?.join(', ') || 'All areas'}\n\n` + `## 📝 Content Strategy\n\n` + recommendations.content_strategy.map(rec => `### ${rec.recommendation}\n` + `**Rationale:** ${rec.rationale}\n` + `**Expected Outcome:** ${rec.expected_outcome}\n` + `**Implementation Effort:** ${rec.implementation_effort}\n\n` ).join('') + `## 👥 Audience Development\n\n` + recommendations.audience_development.map(aud => `### Target: ${aud.target_segment}\n` + `**Approach:** ${aud.approach}\n` + `**Content Suggestions:** ${aud.content_suggestions.join(', ')}\n` + `**Growth Potential:** ${(aud.growth_potential * 100).toFixed(1)}%\n\n` ).join('') + `## 📱 Platform Optimization\n\n` + recommendations.platform_optimization.map(plat => `### ${plat.platform}\n` + `**Current Performance:** ${(plat.current_performance * 100).toFixed(1)}%\n` + `**Optimization Areas:** ${plat.optimization_areas.join(', ')}\n` + `**Actions:** ${plat.specific_actions.join(', ')}\n\n` ).join('') + `## 🏆 Competitive Insights\n\n` + recommendations.competitive_insights.map(comp => `**Insight:** ${comp.insight}\n` + `**Opportunity:** ${comp.opportunity}\n` + `**Strategy:** ${comp.differentiation_strategy}\n\n` ).join(''), }, { type: 'text', text: JSON.stringify(recommendations, null, 2), }, ], }; } /** * Update brand model with new performance data */ async learnFromPerformance(args: z.infer<typeof LearnFromPerformanceArgsSchema>) { const { user_id, video_id, real_metrics, platform } = args; console.log(`Learning from performance data for user: ${user_id}, video: ${video_id}`); const learning_insights = await this.processPerformanceData( user_id, video_id, real_metrics, platform ); return { content: [ { type: 'text', text: `# 🧠 Brand Learning Update\n\n` + `**User:** ${user_id}\n` + `**Video:** ${video_id}\n` + `**Platform:** ${platform}\n\n` + `## 📊 Performance Analysis\n\n` + `- **Views:** ${real_metrics.views.toLocaleString()}\n` + `- **Engagement Rate:** ${(real_metrics.engagement_rate * 100).toFixed(2)}%\n` + `- **Retention Rate:** ${(real_metrics.retention_rate * 100).toFixed(2)}%\n` + `- **Completion Rate:** ${(real_metrics.completion_rate * 100).toFixed(2)}%\n` + `- **Shares:** ${real_metrics.shares}\n` + `- **Comments:** ${real_metrics.comments}\n\n` + `## 🎯 Learning Insights\n\n` + learning_insights.insights.map((insight: any) => `- **${insight.category}:** ${insight.insight}\n` ).join('') + `\n## 🔄 Model Updates\n\n` + learning_insights.model_updates.map((update: any) => `- **${update.component}:** ${update.change} (confidence: ${(update.confidence * 100).toFixed(1)}%)\n` ).join('') + `\n## 🔮 Future Predictions\n\n` + `Based on this performance data, we predict:\n` + learning_insights.predictions.map((pred: any) => `- ${pred.prediction} (${(pred.confidence * 100).toFixed(1)}% confidence)\n` ).join(''), }, { type: 'text', text: JSON.stringify(learning_insights, null, 2), }, ], }; } /** * Get workflow optimization suggestions */ async optimizeWorkflow(args: { user_id: string; workflow_type?: string }) { const { user_id, workflow_type } = args; const optimization = await this.generateWorkflowOptimization(user_id, workflow_type); return { content: [ { type: 'text', text: `# ⚙️ Workflow Optimization Report\n\n` + `**User:** ${user_id}\n` + `**Workflow Type:** ${workflow_type || 'All'}\n\n` + `## 🚀 Optimization Suggestions\n\n` + optimization.suggestions.map((sugg: any) => `### ${sugg.area}\n` + `**Current Efficiency:** ${(sugg.current_efficiency * 100).toFixed(1)}%\n` + `**Potential Improvement:** ${(sugg.potential_improvement * 100).toFixed(1)}%\n` + `**Recommendations:**\n` + sugg.recommendations.map((rec: any) => `- ${rec}\n`).join('') + `\n` ).join(''), }, ], }; } // Private helper methods private async generateBrandAnalysis( user_id: string, time_period: string, include_predictions: boolean, competitive_analysis: boolean ): Promise<BrandAnalysis> { // Simulate comprehensive brand analysis return { brand_evolution: { current_score: 0.78 + Math.random() * 0.15, growth_trend: (Math.random() - 0.3) * 0.2, // -6% to +14% authenticity_rating: 0.88 + Math.random() * 0.1, consistency_score: 0.75 + Math.random() * 0.2, audience_alignment: 0.82 + Math.random() * 0.15, }, performance_metrics: { average_engagement: 0.045 + Math.random() * 0.03, retention_improvement: 0.12 + Math.random() * 0.15, audience_growth: 0.08 + Math.random() * 0.1, content_quality_trend: 0.15 + Math.random() * 0.1, platform_performance: { YouTube: 0.72 + Math.random() * 0.2, LinkedIn: 0.68 + Math.random() * 0.25, Twitter: 0.55 + Math.random() * 0.3, }, }, style_analysis: { communication_style: 'Technical Expert with Approachable Tone', expertise_areas: ['Software Development', 'System Architecture', 'DevOps', 'AI/ML'], tone_consistency: 0.84 + Math.random() * 0.12, visual_branding: 0.71 + Math.random() * 0.2, content_themes: ['Tutorials', 'Best Practices', 'Tool Reviews', 'Architecture Decisions'], }, audience_insights: { primary_demographics: { junior_developers: 0.35, senior_developers: 0.28, tech_leads: 0.22, product_managers: 0.15, }, engagement_patterns: { peak_hours: ['9-11 AM', '2-4 PM', '7-9 PM'], best_days: ['Tuesday', 'Wednesday', 'Thursday'], content_length_preference: '8-15 minutes', }, content_preferences: ['Step-by-step tutorials', 'Real-world examples', 'Problem-solving'], optimal_posting_times: ['Tuesday 10:00 AM', 'Wednesday 2:00 PM', 'Thursday 9:00 AM'], platform_affinity: { YouTube: 0.8, LinkedIn: 0.65, Twitter: 0.4, }, }, recommendations: [ { category: 'Content Strategy', priority: 9, action: 'Create more intermediate-level content to bridge junior-senior gap', expected_impact: 'High engagement increase', timeframe: '2-4 weeks', }, { category: 'Audience Development', priority: 8, action: 'Expand into data science tutorials to attract ML engineers', expected_impact: 'New audience segment', timeframe: '1-2 months', }, { category: 'Platform Optimization', priority: 7, action: 'Improve LinkedIn post formatting for better professional engagement', expected_impact: 'Medium engagement boost', timeframe: '1-2 weeks', }, ], }; } private async generateRecommendations( user_id: string, focus_areas?: string[] ): Promise<BrandRecommendations> { return { content_strategy: [ { recommendation: 'Create Advanced Tutorial Series', rationale: 'Your audience shows high engagement with complex technical content', expected_outcome: '25% increase in watch time and subscriber retention', implementation_effort: 'High', }, { recommendation: 'Add Real-World Case Studies', rationale: 'Professionals prefer practical examples over theoretical explanations', expected_outcome: '15% boost in LinkedIn shares and comments', implementation_effort: 'Medium', }, ], audience_development: [ { target_segment: 'Senior Engineers', approach: 'Focus on architecture decisions and system design', content_suggestions: ['Design patterns', 'Scalability challenges', 'Technical interviews'], growth_potential: 0.82, }, { target_segment: 'Tech Leads', approach: 'Leadership and team management content', content_suggestions: ['Code reviews', 'Team workflows', 'Technical mentoring'], growth_potential: 0.74, }, ], platform_optimization: [ { platform: 'YouTube', current_performance: 0.72, optimization_areas: ['Thumbnail design', 'Title optimization', 'Video descriptions'], specific_actions: ['A/B test thumbnail styles', 'Include keywords in titles', 'Add timestamps'], }, { platform: 'LinkedIn', current_performance: 0.68, optimization_areas: ['Post timing', 'Content formatting', 'Professional networking'], specific_actions: ['Post during business hours', 'Use bullet points', 'Tag relevant professionals'], }, ], competitive_insights: [ { insight: 'Your content is more technical than 80% of competitors', opportunity: 'Capture the advanced tutorial market segment', differentiation_strategy: 'Position as the go-to source for expert-level content', }, { insight: 'Competitors lack real-world project walkthroughs', opportunity: 'Create end-to-end project series', differentiation_strategy: 'Show complete development cycles with real challenges', }, ], }; } private async processPerformanceData( user_id: string, video_id: string, metrics: any, platform: string ) { return { insights: [ { category: 'Engagement Pattern', insight: `${platform} audience prefers ${metrics.completion_rate > 0.7 ? 'longer-form' : 'shorter'} content`, }, { category: 'Content Performance', insight: `Retention rate suggests ${metrics.retention_rate > 0.6 ? 'strong' : 'weak'} content structure`, }, { category: 'Audience Response', insight: `High ${metrics.engagement_rate > 0.05 ? 'engagement' : 'view count'} indicates good ${metrics.engagement_rate > 0.05 ? 'content quality' : 'discoverability'}`, }, ], model_updates: [ { component: 'Platform Preference', change: `Increased ${platform} weight by 5%`, confidence: 0.82, }, { component: 'Content Length Preference', change: 'Adjusted optimal duration based on completion rate', confidence: 0.76, }, ], predictions: [ { prediction: 'Similar content will achieve 15% higher engagement', confidence: 0.73, }, { prediction: `${platform} algorithm will favor this content type`, confidence: 0.68, }, ], }; } private async generateWorkflowOptimization(user_id: string, workflow_type?: string) { return { suggestions: [ { area: 'Content Planning', current_efficiency: 0.65, potential_improvement: 0.25, recommendations: [ 'Batch content planning sessions weekly', 'Use AI-powered topic research', 'Create reusable content templates', ], }, { area: 'Video Production', current_efficiency: 0.78, potential_improvement: 0.18, recommendations: [ 'Automate screen recording with DailyDoco', 'Use AI for initial narration drafts', 'Implement automated editing workflows', ], }, { area: 'Distribution', current_efficiency: 0.55, potential_improvement: 0.35, recommendations: [ 'Schedule cross-platform posting', 'Use analytics for optimal timing', 'Automate social media snippets', ], }, ], }; } }