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MarioDeFelipe

SAP Datasphere MCP Server

query_relational_entity

Execute OData queries on relational entities to extract large batches of data for ETL pipelines. Supports filtering, column selection, and pagination.

Instructions

Execute OData queries on relational entities for ETL data extraction. Supports large batch processing (up to 50,000 records), advanced filtering, column selection, and pagination. Optimized for data warehouse loading and analytics pipelines. Use list_relational_entities to discover available entity names first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
space_idYesSpace identifier (e.g., 'SAP_CONTENT')
asset_idYesAsset identifier - same as used in list_relational_entities (e.g., 'SAP_SC_FI_AM_FINTRANSACTIONS')
entity_nameYesEntity name from the OData service (e.g., 'Results', 'Data'). Use list_relational_entities to get available entity names. If unsure, try using the asset_id as entity_name.
filterNoOData $filter expression (e.g., "amount gt 1000 and status eq 'ACTIVE'")
selectNoComma-separated column list for $select (e.g., "customer_id,amount,date")
topNoMaximum records to return (default: 1000, max: 50000 for ETL)
skipNoNumber of records to skip for pagination
orderbyNoOData $orderby expression (e.g., "amount desc, date asc")
Behavior4/5

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

No annotations provided, so description carries full burden. It mentions supports 50k records, filtering, column selection, pagination, and optimization for ETL. It implies read-only behavior but does not explicitly state it. Given the context, it is sufficiently transparent but could be more explicit about side effects.

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

Conciseness5/5

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

Three sentences, front-loaded with purpose, then details. No wasted words.

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

Completeness3/5

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

Covers purpose, usage, and parameters well. However, since there is no output schema, the description lacks information about the return format or structure of results, which is a gap for a complex 8-parameter tool.

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

Parameters4/5

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

Schema coverage is 100%, baseline 3. Description adds value for parameters like entity_name (suggests trying asset_id) and top (specifies max 50000 for ETL). Provides examples for filter and orderby.

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

Purpose5/5

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

Description clearly states it executes OData queries on relational entities for ETL data extraction, and distinguishes itself from list_relational_entities (which is for discovery). It specifies use case and capabilities.

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

Usage Guidelines4/5

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

Explicitly advises to use list_relational_entities first to discover entity names, providing clear context. However, it does not mention when not to use this tool (e.g., for smaller or real-time queries).

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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