Smartling MCP Server

import { SmartlingClient } from '../smartling-client.js'; export interface CostAnalysisParams { projectId: string; timeframe: 'daily' | 'weekly' | 'monthly' | 'quarterly'; includePredictions: boolean; includeOptimizations: boolean; } export interface CostSummary { totalCost: number; avgCostPerWord: number; avgCostPerLocale: number; totalWords: number; totalLocales: number; costBreakdown: { translation: number; editing: number; proofreading: number; other: number; }; } export interface CostBreakdown { byLocale: Array<{ locale: string; cost: number; words: number; avgRate: number; }>; byContentType: Array<{ type: string; cost: number; percentage: number; }>; byTimeperiod: Array<{ period: string; cost: number; trend: 'up' | 'down' | 'stable'; }>; } export interface CostTrends { monthlyTrend: 'increasing' | 'decreasing' | 'stable'; seasonality: Array<{ month: string; avgCost: number; deviation: number; }>; peakPeriods: Array<{ period: string; cost: number; reason: string; }>; } export interface CostPredictions { nextMonth: { estimatedCost: number; confidence: number; factors: string[]; }; nextQuarter: { estimatedCost: number; confidence: number; budgetRecommendation: number; }; yearEnd: { projectedTotal: number; confidence: number; varianceRange: { min: number; max: number; }; }; } export interface CostOptimizations { suggestions: Array<{ type: string; description: string; potentialSavings: number; effort: 'low' | 'medium' | 'high'; timeframe: string; implementation: string[]; }>; bundlingOpportunities: Array<{ locales: string[]; estimatedSavings: number; description: string; }>; workflowOptimizations: Array<{ currentStep: string; optimizedStep: string; savingsPercentage: number; description: string; }>; } export interface CostAnalysisResult { summary: CostSummary; breakdown: CostBreakdown; trends: CostTrends; predictions?: CostPredictions; optimizations?: CostOptimizations; potentialSavings: number; } /** * AI-Powered Cost Analyzer * Provides intelligent cost analysis, predictions, and optimization suggestions */ export class CostAnalyzer { constructor(private smartlingClient: SmartlingClient) {} /** * Perform comprehensive cost analysis */ async analyzeCosts(params: CostAnalysisParams): Promise<CostAnalysisResult> { try { // Gather cost data from multiple sources const [ billingData, projectData, historicalCosts, usageMetrics ] = await Promise.all([ this.getBillingData(params.projectId, params.timeframe), this.getProjectData(params.projectId), this.getHistoricalCosts(params.projectId, params.timeframe), this.getUsageMetrics(params.projectId, params.timeframe) ]); // Generate summary const summary = this.generateCostSummary(billingData, usageMetrics); // Create breakdown analysis const breakdown = this.createCostBreakdown(billingData, projectData, params.timeframe); // Analyze trends const trends = this.analyzeCostTrends(historicalCosts, params.timeframe); let predictions: CostPredictions | undefined; let optimizations: CostOptimizations | undefined; // Generate predictions if requested if (params.includePredictions) { predictions = await this.generateCostPredictions( historicalCosts, projectData, trends ); } // Generate optimization suggestions if requested if (params.includeOptimizations) { optimizations = await this.generateOptimizations( summary, breakdown, trends, projectData ); } // Calculate potential savings const potentialSavings = this.calculatePotentialSavings(optimizations); return { summary, breakdown, trends, predictions, optimizations, potentialSavings }; } catch (error) { console.error('Cost analysis failed:', error); throw new Error(`Failed to analyze costs: ${error instanceof Error ? error.message : String(error)}`); } } /** * Generate cost predictions using AI models */ async generateCostPredictions( historicalCosts: any[], projectData: any, trends: CostTrends ): Promise<CostPredictions> { try { // Simple prediction algorithm (could be enhanced with ML models) const recentAverage = this.calculateRecentAverage(historicalCosts, 30); // 30 days const trendMultiplier = this.getTrendMultiplier(trends.monthlyTrend); const seasonalityFactor = this.getSeasonalityFactor(trends.seasonality); const nextMonthBase = recentAverage * trendMultiplier * seasonalityFactor; const nextQuarterBase = nextMonthBase * 3; const yearEndBase = this.calculateYearEndProjection(historicalCosts, trends); return { nextMonth: { estimatedCost: Math.round(nextMonthBase * 100) / 100, confidence: this.calculateConfidence(historicalCosts, 'monthly'), factors: this.identifyPredictionFactors(trends, projectData) }, nextQuarter: { estimatedCost: Math.round(nextQuarterBase * 100) / 100, confidence: this.calculateConfidence(historicalCosts, 'quarterly'), budgetRecommendation: Math.round(nextQuarterBase * 1.15 * 100) / 100 // 15% buffer }, yearEnd: { projectedTotal: Math.round(yearEndBase * 100) / 100, confidence: this.calculateConfidence(historicalCosts, 'yearly'), varianceRange: { min: Math.round(yearEndBase * 0.85 * 100) / 100, max: Math.round(yearEndBase * 1.25 * 100) / 100 } } }; } catch (error) { console.error('Cost prediction failed:', error); // Return fallback predictions return { nextMonth: { estimatedCost: 0, confidence: 0, factors: ['Prediction unavailable - insufficient data'] }, nextQuarter: { estimatedCost: 0, confidence: 0, budgetRecommendation: 0 }, yearEnd: { projectedTotal: 0, confidence: 0, varianceRange: { min: 0, max: 0 } } }; } } /** * Generate AI-powered optimization suggestions */ async generateOptimizations( summary: CostSummary, breakdown: CostBreakdown, trends: CostTrends, projectData: any ): Promise<CostOptimizations> { try { const suggestions: CostOptimizations['suggestions'] = []; const bundlingOpportunities: CostOptimizations['bundlingOpportunities'] = []; const workflowOptimizations: CostOptimizations['workflowOptimizations'] = []; // Analyze rate optimization opportunities if (summary.avgCostPerWord > 0.15) { suggestions.push({ type: 'rate_negotiation', description: 'Your current average rate per word is above market average. Consider negotiating volume discounts.', potentialSavings: summary.totalCost * 0.15, effort: 'medium', timeframe: '2-4 weeks', implementation: [ 'Analyze historical volume', 'Prepare cost comparison report', 'Schedule negotiation with account manager' ] }); } // Identify locale bundling opportunities const localeGroups = this.identifyLocaleBundles(breakdown.byLocale); for (const group of localeGroups) { bundlingOpportunities.push({ locales: group.locales, estimatedSavings: group.savings, description: `Bundle ${group.locales.join(', ')} for ${Math.round(group.savingsPercent)}% savings` }); } // Workflow optimization suggestions if (summary.costBreakdown.editing > summary.costBreakdown.translation * 0.3) { workflowOptimizations.push({ currentStep: 'Manual editing after translation', optimizedStep: 'Enhanced QA during translation', savingsPercentage: 20, description: 'High editing costs suggest quality issues during translation. Implementing better QA upfront could reduce editing needs.' }); } // Content type optimization const expensiveContent = breakdown.byContentType .filter(item => item.cost > summary.totalCost * 0.25) .sort((a, b) => b.cost - a.cost); for (const content of expensiveContent) { suggestions.push({ type: 'content_optimization', description: `${content.type} content represents ${Math.round(content.percentage)}% of costs. Consider optimization strategies.`, potentialSavings: content.cost * 0.10, effort: 'medium', timeframe: '1-2 months', implementation: [ `Analyze ${content.type} content patterns`, 'Implement terminology management', 'Create content guidelines', 'Train content creators' ] }); } // Seasonal optimization if (trends.seasonality.length > 0) { const peakCost = Math.max(...trends.seasonality.map(s => s.avgCost)); const lowCost = Math.min(...trends.seasonality.map(s => s.avgCost)); if ((peakCost - lowCost) / lowCost > 0.3) { suggestions.push({ type: 'seasonal_planning', description: 'Significant seasonal cost variation detected. Plan translations during low-cost periods.', potentialSavings: (peakCost - lowCost) * 0.5, effort: 'low', timeframe: 'Ongoing', implementation: [ 'Identify seasonal patterns', 'Create translation calendar', 'Pre-plan high-volume periods', 'Negotiate seasonal rates' ] }); } } // Technology optimization suggestions.push({ type: 'automation', description: 'Implement translation automation for repetitive content types.', potentialSavings: summary.totalCost * 0.25, effort: 'high', timeframe: '3-6 months', implementation: [ 'Audit repetitive content', 'Implement TM leverage', 'Set up automated workflows', 'Train team on new processes' ] }); return { suggestions, bundlingOpportunities, workflowOptimizations }; } catch (error) { console.error('Optimization generation failed:', error); return { suggestions: [{ type: 'manual_review', description: 'Automated optimization analysis unavailable. Manual cost review recommended.', potentialSavings: 0, effort: 'medium', timeframe: '1-2 weeks', implementation: ['Contact account manager for manual cost review'] }], bundlingOpportunities: [], workflowOptimizations: [] }; } } // ===== PRIVATE HELPER METHODS ===== private async getBillingData(projectId: string, timeframe: string): Promise<any[]> { try { // This would integrate with Smartling billing API // For now, return mock data structure return [ { period: '2024-01', totalCost: 1250.50, words: 12500, locales: ['es-ES', 'fr-FR', 'de-DE'], breakdown: { translation: 1000.00, editing: 200.00, proofreading: 50.50 } } ]; } catch (error) { console.error('Failed to get billing data:', error); return []; } } private async getProjectData(projectId: string): Promise<any> { try { return await this.smartlingClient.getProject(projectId); } catch (error) { console.error('Failed to get project data:', error); return {}; } } private async getHistoricalCosts(projectId: string, timeframe: string): Promise<any[]> { // This would fetch historical cost data // For now, return mock data return Array.from({ length: 12 }, (_, i) => ({ month: 2024 - Math.floor(i / 12), period: String(((11 - i) % 12) + 1).padStart(2, '0'), cost: 1000 + Math.random() * 500, words: 10000 + Math.random() * 5000 })); } private async getUsageMetrics(projectId: string, timeframe: string): Promise<any> { // This would fetch usage metrics return { totalWords: 50000, totalJobs: 25, averageJobSize: 2000, utilizationRate: 0.85 }; } private generateCostSummary(billingData: any[], usageMetrics: any): CostSummary { const totalCost = billingData.reduce((sum, item) => sum + item.totalCost, 0); const totalWords = usageMetrics.totalWords || 0; const totalLocales = billingData.reduce((count, item) => count + item.locales.length, 0); const costBreakdown = billingData.reduce((acc, item) => ({ translation: acc.translation + (item.breakdown?.translation || 0), editing: acc.editing + (item.breakdown?.editing || 0), proofreading: acc.proofreading + (item.breakdown?.proofreading || 0), other: acc.other + (item.breakdown?.other || 0) }), { translation: 0, editing: 0, proofreading: 0, other: 0 }); return { totalCost, avgCostPerWord: totalWords > 0 ? totalCost / totalWords : 0, avgCostPerLocale: totalLocales > 0 ? totalCost / totalLocales : 0, totalWords, totalLocales, costBreakdown }; } private createCostBreakdown(billingData: any[], projectData: any, timeframe: string): CostBreakdown { // Create locale breakdown const localeMap = new Map<string, { cost: number; words: number; }>(); billingData.forEach(item => { item.locales.forEach((locale: string) => { const existing = localeMap.get(locale) || { cost: 0, words: 0 }; localeMap.set(locale, { cost: existing.cost + (item.totalCost / item.locales.length), words: existing.words + (item.words / item.locales.length) }); }); }); const byLocale = Array.from(localeMap.entries()).map(([locale, data]) => ({ locale, cost: Math.round(data.cost * 100) / 100, words: Math.round(data.words), avgRate: data.words > 0 ? Math.round((data.cost / data.words) * 10000) / 10000 : 0 })); // Create content type breakdown (mock data) const byContentType = [ { type: 'Marketing', cost: 500, percentage: 40 }, { type: 'Documentation', cost: 400, percentage: 32 }, { type: 'UI/UX', cost: 200, percentage: 16 }, { type: 'Legal', cost: 150, percentage: 12 } ]; // Create time period breakdown const byTimeperiod = billingData.map(item => ({ period: item.period, cost: item.totalCost, trend: this.calculateTrend(billingData, item.period) as 'up' | 'down' | 'stable' })); return { byLocale, byContentType, byTimeperiod }; } private analyzeCostTrends(historicalCosts: any[], timeframe: string): CostTrends { const monthlyCosts = historicalCosts.slice(-12); // Last 12 months // Calculate monthly trend const recentCosts = monthlyCosts.slice(-3).map(item => item.cost); const olderCosts = monthlyCosts.slice(-6, -3).map(item => item.cost); const recentAvg = recentCosts.reduce((a, b) => a + b, 0) / recentCosts.length; const olderAvg = olderCosts.reduce((a, b) => a + b, 0) / olderCosts.length; let monthlyTrend: 'increasing' | 'decreasing' | 'stable' = 'stable'; if (recentAvg > olderAvg * 1.1) monthlyTrend = 'increasing'; else if (recentAvg < olderAvg * 0.9) monthlyTrend = 'decreasing'; // Calculate seasonality const seasonality = monthlyCosts.map(item => { const avg = monthlyCosts.reduce((a, b) => a + b.cost, 0) / monthlyCosts.length; return { month: `${item.month}-${item.period}`, avgCost: item.cost, deviation: ((item.cost - avg) / avg) * 100 }; }); // Identify peak periods const avgCost = monthlyCosts.reduce((a, b) => a + b.cost, 0) / monthlyCosts.length; const peakPeriods = monthlyCosts .filter(item => item.cost > avgCost * 1.2) .map(item => ({ period: `${item.month}-${item.period}`, cost: item.cost, reason: 'High translation volume' })); return { monthlyTrend, seasonality, peakPeriods }; } private calculateRecentAverage(costs: any[], days: number): number { const recent = costs.slice(-Math.ceil(days / 30)); // Approximate months return recent.reduce((sum, item) => sum + item.cost, 0) / recent.length; } private getTrendMultiplier(trend: string): number { switch (trend) { case 'increasing': return 1.1; case 'decreasing': return 0.9; default: return 1.0; } } private getSeasonalityFactor(seasonality: any[]): number { const currentMonth = new Date().getMonth(); const seasonal = seasonality[currentMonth % seasonality.length]; return seasonal ? 1 + (seasonal.deviation / 100) : 1.0; } private calculateYearEndProjection(historicalCosts: any[], trends: CostTrends): number { const monthlyAverage = historicalCosts.reduce((sum, item) => sum + item.cost, 0) / historicalCosts.length; const remainingMonths = 12 - new Date().getMonth(); const trendMultiplier = this.getTrendMultiplier(trends.monthlyTrend); return monthlyAverage * remainingMonths * trendMultiplier; } private calculateConfidence(historicalCosts: any[], period: string): number { const variance = this.calculateVariance(historicalCosts.map(item => item.cost)); const mean = historicalCosts.reduce((sum, item) => sum + item.cost, 0) / historicalCosts.length; const cv = Math.sqrt(variance) / mean; // Coefficient of variation // Higher confidence for lower variability return Math.max(0.1, Math.min(0.95, 1 - cv)); } private calculateVariance(numbers: number[]): number { const mean = numbers.reduce((a, b) => a + b, 0) / numbers.length; return numbers.reduce((sum, num) => sum + Math.pow(num - mean, 2), 0) / numbers.length; } private identifyPredictionFactors(trends: CostTrends, projectData: any): string[] { const factors = []; if (trends.monthlyTrend === 'increasing') { factors.push('Increasing cost trend detected'); } if (trends.peakPeriods.length > 0) { factors.push('Seasonal peaks identified'); } if (projectData.upcomingReleases) { factors.push('Upcoming product releases'); } return factors.length > 0 ? factors : ['Historical patterns', 'Current project scope']; } private identifyLocaleBundles(localeData: any[]): Array<{ locales: string[]; savings: number; savingsPercent: number; }> { const bundles = []; // Simple bundling logic - similar language families const languageFamilies = { romance: ['es-ES', 'fr-FR', 'it-IT', 'pt-BR'], germanic: ['de-DE', 'nl-NL', 'da-DK', 'sv-SE'], slavic: ['ru-RU', 'pl-PL', 'cs-CZ'] }; for (const [family, locales] of Object.entries(languageFamilies)) { const availableLocales = localeData .filter(item => locales.includes(item.locale)) .map(item => item.locale); if (availableLocales.length >= 2) { const totalCost = localeData .filter(item => availableLocales.includes(item.locale)) .reduce((sum, item) => sum + item.cost, 0); bundles.push({ locales: availableLocales, savings: totalCost * 0.15, // 15% bundle discount savingsPercent: 15 }); } } return bundles; } private calculateTrend(data: any[], currentPeriod: string): string { const currentIndex = data.findIndex(item => item.period === currentPeriod); if (currentIndex <= 0) return 'stable'; const current = data[currentIndex].totalCost; const previous = data[currentIndex - 1].totalCost; if (current > previous * 1.05) return 'up'; if (current < previous * 0.95) return 'down'; return 'stable'; } private calculatePotentialSavings(optimizations?: CostOptimizations): number { if (!optimizations) return 0; const suggestionSavings = optimizations.suggestions.reduce((sum, s) => sum + s.potentialSavings, 0); const bundlingSavings = optimizations.bundlingOpportunities.reduce((sum, b) => sum + b.estimatedSavings, 0); return Math.round((suggestionSavings + bundlingSavings) * 100) / 100; } }