Run predictive analytics on SAP data

Instructions

Implementation Reference

• src/tools/realtime-tools.ts:445-561 (handler)

  The primary handler function implementing the tool's logic for actions: predict, train, evaluate, list_models, get_model. Dispatches to helper methods and returns structured responses.

  public async execute(args: z.infer<typeof this.inputSchema>): Promise<any> {
    logger.info('Running predictive analytics', {
      action: args.action,
      entityType: args.entityType,
      targetMetric: args.targetMetric,
    });
  
    try {
      switch (args.action) {
        case 'predict':
          if (!args.entityType || !args.serviceId || !args.targetMetric || !args.forecastPeriod) {
            throw new Error(
              'entityType, serviceId, targetMetric, and forecastPeriod are required for prediction'
            );
          }
  
          const prediction = this.generatePrediction(args);
  
          return {
            success: true,
            prediction,
            algorithm: args.algorithm === 'auto_select' ? 'time_series' : args.algorithm,
            confidence: 0.85,
            modelMetrics: {
              accuracy: 0.92,
              meanAbsoluteError: 0.08,
              rmse: 0.12,
            },
            recommendations: this.generateRecommendations(prediction),
            insights: [
              'Strong upward trend detected in recent data',
              'Seasonal pattern identified with weekly cycles',
              'Confidence decreases for longer forecast horizons',
            ],
          };
  
        case 'train':
          if (!args.entityType || !args.serviceId || !args.targetMetric) {
            throw new Error('entityType, serviceId, and targetMetric are required for training');
          }
  
          const modelId = `model_${Date.now()}_${Math.random().toString(36).substr(2, 6)}`;
  
          return {
            success: true,
            message: 'Model training initiated',
            modelId,
            trainingConfig: {
              entityType: args.entityType,
              serviceId: args.serviceId,
              targetMetric: args.targetMetric,
              algorithm: args.algorithm === 'auto_select' ? 'time_series' : args.algorithm,
              trainingDataDays: args.trainingDataDays,
              features: args.features || ['timestamp', 'value', 'category'],
            },
            estimatedTime: '5-10 minutes',
            status: 'training',
            progress: 0,
          };
  
        case 'list_models':
          const models = this.generateMockModels();
  
          return {
            success: true,
            models,
            total: models.length,
            summary: `Found ${models.length} trained predictive models`,
          };
  
        case 'evaluate':
          if (!args.modelId) {
            throw new Error('Model ID is required for evaluation');
          }
  
          const evaluation = this.generateModelEvaluation(args.modelId);
  
          return {
            success: true,
            modelId: args.modelId,
            evaluation,
            recommendations: [
              'Model shows good accuracy for short-term predictions',
              'Consider retraining with more recent data',
              'Monitor prediction accuracy in production',
            ],
          };
  
        case 'get_model':
          if (!args.modelId) {
            throw new Error('Model ID is required');
          }
  
          const modelDetails = this.generateModelDetails(args.modelId);
  
          return {
            success: true,
            model: modelDetails,
          };
  
        default:
          throw new Error(`Unknown action: ${args.action}`);
      }
    } catch (error: any) {
      logger.error('Predictive analytics error', { error: error.message });
      return {
        success: false,
        error: error.message,
        action: args.action,
        troubleshooting: [
          'Ensure sufficient historical data exists for training',
          'Verify entity types and metrics are valid',
          'Check that target metrics contain numeric data',
        ],
      };
    }
  }

• src/tools/realtime-tools.ts:407-441 (schema)

  Zod schema defining the input parameters for the tool, including action types and prediction configuration options.

  public readonly inputSchema = z
    .object({
      action: z.enum(['predict', 'train', 'evaluate', 'list_models', 'get_model']),
      entityType: z.string().optional().describe('SAP entity type for prediction'),
      serviceId: z.string().optional().describe('SAP service ID'),
      targetMetric: z
        .string()
        .optional()
        .describe('Metric to predict (e.g., revenue, count, average)'),
      forecastPeriod: z
        .object({
          period: z.enum(['hours', 'days', 'weeks', 'months']),
          size: z.number().positive(),
        })
        .optional()
        .describe('Prediction time horizon'),
      algorithm: z
        .enum(['linear_regression', 'time_series', 'neural_network', 'auto_select'])
        .optional()
        .default('auto_select')
        .describe('ML algorithm to use'),
      features: z.array(z.string()).optional().describe('Input features for prediction'),
      modelId: z.string().optional().describe('Existing model ID (for evaluate/get_model)'),
      includeConfidenceBounds: z
        .boolean()
        .optional()
        .default(true)
        .describe('Include prediction confidence intervals'),
      trainingDataDays: z
        .number()
        .optional()
        .default(30)
        .describe('Days of historical data for training'),
    })
    .describe('Predictive analytics configuration');

• src/tools/realtime-tools.ts:1122-1127 (registration)

  Registration of the PredictiveAnalyticsEngineTool instance in the realtimeAnalyticsTools export array, making it available for import and use in the MCP tool system.

  export const realtimeAnalyticsTools = [
    new RealTimeDataStreamTool(),
    new KPIDashboardBuilderTool(),
    new PredictiveAnalyticsEngineTool(),
    new BusinessIntelligenceInsightsTool(),
  ];

• src/tools/realtime-tools.ts:563-603 (helper)

  Key helper method that generates mock predictive forecasts using trend, seasonality, and noise simulation for time series prediction.

  private generatePrediction(args: any): any {
    const baseValue = Math.random() * 1000 + 500;
    const predictions = [];
  
    for (let i = 1; i <= args.forecastPeriod.size; i++) {
      const trend = (Math.random() - 0.4) * 0.1; // Slight upward bias
      const seasonality = Math.sin((i * Math.PI) / 7) * 0.1; // Weekly pattern
      const noise = (Math.random() - 0.5) * 0.05; // Small random variation
  
      const multiplier = 1 + trend * i + seasonality + noise;
      const value = baseValue * multiplier;
      const confidence = Math.max(0.6, 0.95 - i * 0.05); // Decreasing confidence
  
      predictions.push({
        period: i,
        timestamp: this.addTime(new Date(), args.forecastPeriod.period, i),
        value: Math.round(value),
        confidence,
        bounds: args.includeConfidenceBounds
          ? {
              lower: Math.round(value * (1 - (1 - confidence))),
              upper: Math.round(value * (1 + (1 - confidence))),
            }
          : undefined,
      });
    }
  
    return {
      targetMetric: args.targetMetric,
      entityType: args.entityType,
      forecastPeriod: args.forecastPeriod,
      predictions,
      summary: {
        trend: 'increasing',
        avgGrowthRate: '2.3%',
        volatility: 'low',
        seasonality: 'weekly pattern detected',
      },
      generated: new Date().toISOString(),
    };
  }