SAP OData to MCP Server

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(), }; }