SAP OData to MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| search-sap-servicesC | Find SAP services by keyword/category. |
| discover-service-entitiesA | List all entities and their capabilities within a specific SAP service. Use this after finding a service to understand what data you can work with. |
| get-entity-schemaC | Get detailed schema information for a specific entity including properties, types, keys, and constraints. |
| execute-entity-operationA | ⚠️ Direct CRUD operations on SAP entities with precise OData queries. Use ONLY when you have exact OData query syntax (not natural language). For natural language queries, use natural-query-builder FIRST. Requires authentication. |
| check-sap-authenticationC | 🔐 Validate/associate authentication session. Call with session_id to authenticate. |
| sap-smart-queryC | Routes SAP requests to optimal tool |
| natural-query-builderC | Convert natural language to OData queries |
| smart-data-analysisC | Analyze SAP data patterns, trends, and generate actionable business insights with AI-powered statistical analysis. Provides automated data exploration and visualization recommendations. |
| query-performance-optimizerA | Optimize SAP OData query performance by analyzing execution patterns and suggesting improvements. Automatically identifies bottlenecks and recommends index strategies. |
| business-process-insightsC | Extract business process insights from SAP transactional data using AI pattern recognition. Identifies workflow inefficiencies and automation opportunities. |
| realtime-data-streamD | Stream SAP data via WebSocket |
| kpi-dashboard-builderD | Create and manage KPI dashboards |
| predictive-analytics-engineD | Run predictive analytics on SAP data |
| business-intelligence-insightsD | Generate business insights from SAP data |
| ui-form-generatorC | Creates dynamic forms for SAP entity operations with validation and SAP Fiori styling |
| ui-data-gridC | Creates interactive data grids with sorting, filtering, and export capabilities |
| ui-dashboard-composerC | Creates comprehensive KPI dashboards with charts and real-time data |
| ui-workflow-builderC | Creates visual workflow processes with step-by-step forms and approvals |
| ui-report-builderC | Creates comprehensive drill-down reports with analytical capabilities |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| sap-services | List of all discovered SAP OData services |
TDQS
Scored across 19 tools
The tool set has clear distinctions in some areas, such as authentication, service discovery, and UI components, but there is significant overlap and ambiguity in others. For example, 'business-intelligence-insights', 'business-process-insights', and 'smart-data-analysis' all involve AI-powered analysis of SAP data, making it difficult for an agent to choose the right one without deeper context. Similarly, 'kpi-dashboard-builder' and 'ui-dashboard-composer' both handle dashboard creation, leading to potential confusion.
The naming conventions are inconsistent and chaotic, with no discernible pattern across the tools. There is a mix of styles, such as hyphenated names (e.g., 'business-intelligence-insights'), snake_case (e.g., 'check-sap-authentication' uses hyphens but resembles snake_case), and varied verb usage (e.g., 'discover-service-entities' vs. 'search-sap-services'). Some tools use descriptive phrases rather than clear verb_noun patterns, making the set hard to navigate predictably.
With 19 tools, the count is borderline high for a server focused on SAP OData integration, as it includes both core data operations and extensive UI components, which might be better split into separate servers. While the scope is broad, the number feels heavy and could overwhelm an agent, especially given the overlaps. A more streamlined set of 10-15 tools might be more appropriate for this domain.
The tool surface is largely complete for the SAP OData domain, covering authentication, service discovery, CRUD operations, query building, analytics, and UI components. However, there are minor gaps, such as no explicit tool for deleting or updating data in a natural language context (relying on 'execute-entity-operation' with OData syntax), and the UI tools might be overly detailed for a data-focused server. Overall, agents can work around these gaps, and core workflows are well-supported.