SAP OData to MCP Server

Instructions

Implementation Reference

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

  The execute method of PredictiveAnalyticsEngineTool class implements the core logic for the 'predictive-analytics-engine' tool. It handles actions like 'predict', 'train', 'evaluate', 'list_models', and 'get_model' with comprehensive error handling and mock ML predictions.
  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 input schema definition for the predictive-analytics-engine tool, validating parameters for different actions including prediction, training, and model management.
  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 array, which is likely imported and used in the main tool registry.
  export const realtimeAnalyticsTools = [ new RealTimeDataStreamTool(), new KPIDashboardBuilderTool(), new PredictiveAnalyticsEngineTool(), new BusinessIntelligenceInsightsTool(), ];
• src/middleware/intelligent-tool-router.ts:226-234 (registration)

  Tool name mapping to workflow sequence 'predictiveAnalytics' in the intelligent tool router middleware for optimized execution sequences.
  const sequenceMap: { [key: string]: string } = { 'natural-query-builder': 'naturalLanguageAnalytics', 'search-sap-services': 'dataExploration', 'discover-service-entities': 'dataExploration', 'execute-entity-operation': 'directExecution', 'monitor-query-performance': 'performanceAnalysis', 'realtime-data-stream': 'realtimeData', 'predictive-analytics-engine': 'predictiveAnalytics', };