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Raistlin82

SAP OData to MCP Server

by Raistlin82

Generate business insights from SAP data

business-intelligence-insights

Generate business insights from SAP data to identify trends, anomalies, and optimization opportunities using comprehensive analysis.

Instructions

Generate business insights from SAP data

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYes
entityTypesNoSAP entity types to analyze
serviceIdsNoSAP service IDs to include
analysisTypeNoType of analysis to performcomprehensive
timeWindowNoTime window for analysis
insightIdNoSpecific insight ID (for get action)
minConfidenceNoMinimum confidence threshold for insights
includeRecommendationsNoInclude actionable recommendations
exportFormatNoExport format (for export action)json

Implementation Reference

  • Main handler function that processes input arguments and dispatches to specific actions (generate, list, get, configure, export) for business intelligence insights, using RealtimeAnalyticsService and generating mock insights.
    public async execute(args: z.infer<typeof this.inputSchema>): Promise<any> {
      logger.info('Generating business intelligence insights', {
        action: args.action,
        analysisType: args.analysisType,
      });
    
      const service = getRealtimeService();
    
      try {
        switch (args.action) {
          case 'generate':
            const insights = this.generateBusinessInsights(args);
    
            return {
              success: true,
              insights,
              analysis: {
                type: args.analysisType,
                timeWindow: args.timeWindow,
                entityTypes: args.entityTypes || ['SalesOrder', 'Product', 'Customer'],
                confidence: 0.85,
                dataPoints: Math.floor(Math.random() * 10000) + 5000,
              },
              summary: {
                totalInsights: insights.length,
                highPriority: insights.filter(i => i.severity === 'critical' || i.severity === 'high')
                  .length,
                categories: this.categorizeInsights(insights),
                generatedAt: new Date().toISOString(),
              },
              recommendations: this.generateGlobalRecommendations(insights),
            };
    
          case 'list':
            const existingInsights = service.getInsights();
    
            return {
              success: true,
              insights: existingInsights.map(insight => ({
                insightId: insight.insightId,
                title: insight.title,
                type: insight.type,
                severity: insight.severity,
                confidence: insight.confidence,
                generated: insight.generated,
                expires: insight.expires,
              })),
              total: existingInsights.length,
              active: existingInsights.filter(i => !i.expires || i.expires > new Date()).length,
            };
    
          case 'get':
            if (!args.insightId) {
              throw new Error('Insight ID is required for get action');
            }
    
            const insight = service.getInsights().find(i => i.insightId === args.insightId);
    
            if (!insight) {
              return {
                success: false,
                error: 'Insight not found',
                insightId: args.insightId,
              };
            }
    
            return {
              success: true,
              insight: {
                ...insight,
                relatedInsights: this.findRelatedInsights(insight, service.getInsights()),
                actionsPlan: this.generateActionPlan(insight),
              },
            };
    
          case 'configure':
            const configuration = this.generateInsightConfiguration(args);
    
            return {
              success: true,
              message: 'Business intelligence engine configured',
              configuration,
              status: 'active',
            };
    
          case 'export':
            const exportData = this.generateExportData(service.getInsights(), args.exportFormat!);
    
            return {
              success: true,
              export: exportData,
              format: args.exportFormat,
              recordCount: service.getInsights().length,
              generatedAt: new Date().toISOString(),
            };
    
          default:
            throw new Error(`Unknown action: ${args.action}`);
        }
      } catch (error: any) {
        logger.error('Business intelligence error', { error: error.message });
        return {
          success: false,
          error: error.message,
          action: args.action,
          troubleshooting: [
            'Ensure sufficient historical data exists for analysis',
            'Verify entity types and service IDs are accessible',
            'Check minimum confidence threshold settings',
          ],
        };
      }
    }
  • Zod input schema defining parameters for the tool, including action, entityTypes, serviceIds, analysisType, timeWindow, etc.
    public readonly inputSchema = z
      .object({
        action: z.enum(['generate', 'list', 'get', 'configure', 'export']),
        entityTypes: z.array(z.string()).optional().describe('SAP entity types to analyze'),
        serviceIds: z.array(z.string()).optional().describe('SAP service IDs to include'),
        analysisType: z
          .enum(['trend', 'anomaly', 'correlation', 'optimization', 'comprehensive'])
          .optional()
          .default('comprehensive')
          .describe('Type of analysis to perform'),
        timeWindow: z
          .object({
            period: z.enum(['hours', 'days', 'weeks', 'months']),
            size: z.number().positive(),
          })
          .optional()
          .default({ period: 'days', size: 7 })
          .describe('Time window for analysis'),
        insightId: z.string().optional().describe('Specific insight ID (for get action)'),
        minConfidence: z
          .number()
          .min(0)
          .max(1)
          .optional()
          .default(0.7)
          .describe('Minimum confidence threshold for insights'),
        includeRecommendations: z
          .boolean()
          .optional()
          .default(true)
          .describe('Include actionable recommendations'),
        exportFormat: z
          .enum(['json', 'csv', 'pdf'])
          .optional()
          .default('json')
          .describe('Export format (for export action)'),
      })
      .describe('Business intelligence insights configuration');
  • Registration of the BusinessIntelligenceInsightsTool instance in the realtimeAnalyticsTools array, which is likely imported and registered with the MCP server elsewhere.
    export const realtimeAnalyticsTools = [
      new RealTimeDataStreamTool(),
      new KPIDashboardBuilderTool(),
      new PredictiveAnalyticsEngineTool(),
      new BusinessIntelligenceInsightsTool(),
    ];
  • Key helper method that generates the actual business insights (trend, anomaly, correlation, optimization) based on input parameters.
    private generateBusinessInsights(args: any): any[] {
      const insights = [];
      const analysisTypes =
        args.analysisType === 'comprehensive'
          ? ['trend', 'anomaly', 'correlation', 'optimization']
          : [args.analysisType];
    
      for (const type of analysisTypes) {
        switch (type) {
          case 'trend':
            insights.push(...this.generateTrendInsights());
            break;
          case 'anomaly':
            insights.push(...this.generateAnomalyInsights());
            break;
          case 'correlation':
            insights.push(...this.generateCorrelationInsights());
            break;
          case 'optimization':
            insights.push(...this.generateOptimizationInsights());
            break;
        }
      }
    
      // Filter by confidence threshold
      return insights.filter(insight => insight.confidence >= args.minConfidence);
    }
  • Tool metadata: name and description definition.
    public readonly name = 'business-intelligence-insights';
    public readonly description = 'Generate business insights from SAP data';
    
    public readonly inputSchema = z
      .object({
        action: z.enum(['generate', 'list', 'get', 'configure', 'export']),
        entityTypes: z.array(z.string()).optional().describe('SAP entity types to analyze'),
        serviceIds: z.array(z.string()).optional().describe('SAP service IDs to include'),
        analysisType: z
          .enum(['trend', 'anomaly', 'correlation', 'optimization', 'comprehensive'])
          .optional()
          .default('comprehensive')
          .describe('Type of analysis to perform'),
        timeWindow: z
          .object({
            period: z.enum(['hours', 'days', 'weeks', 'months']),
            size: z.number().positive(),
          })
          .optional()
          .default({ period: 'days', size: 7 })
          .describe('Time window for analysis'),
        insightId: z.string().optional().describe('Specific insight ID (for get action)'),
        minConfidence: z
          .number()
          .min(0)
          .max(1)
          .optional()
          .default(0.7)
          .describe('Minimum confidence threshold for insights'),
        includeRecommendations: z
          .boolean()
          .optional()
          .default(true)
          .describe('Include actionable recommendations'),
        exportFormat: z
          .enum(['json', 'csv', 'pdf'])
          .optional()
          .default('json')
          .describe('Export format (for export action)'),
      })
      .describe('Business intelligence insights configuration');
Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure but offers none. It doesn't indicate whether this is a read-only or mutating operation, what permissions might be required, whether it's computationally intensive, what the output format might be, or any rate limits. For a tool with 9 parameters including complex nested objects, this lack of behavioral context is a critical gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

While technically concise with just one sentence, this represents under-specification rather than effective brevity. The single sentence 'Generate business insights from SAP data' doesn't earn its place by providing meaningful guidance or context. A truly concise description would efficiently convey essential information; this merely states the obvious without adding value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex tool with 9 parameters, no annotations, no output schema, and numerous sibling alternatives, the description is completely inadequate. It fails to address the tool's behavioral characteristics, usage context, parameter relationships, or output expectations. The agent would struggle to understand when and how to use this tool effectively given the minimal information provided.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is high at 89%, so most parameters are documented in the schema itself. The description adds no additional parameter information beyond what's in the schema - it doesn't explain relationships between parameters, provide examples of valid values, or clarify how parameters interact. Given the high schema coverage, the baseline score of 3 is appropriate, but the description fails to compensate for the remaining 11% coverage gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tautological: description restates name/title.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides absolutely no guidance on when to use this tool versus alternatives. With numerous sibling tools that appear related (smart-data-analysis, predictive-analytics-engine, kpi-dashboard-builder, etc.), there is no indication of this tool's specific use cases, prerequisites, or differentiation. The agent receives no help in selecting this tool over other analytical options.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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