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MarioDeFelipe

SAP Datasphere MCP Server

query_relational_entity

Run OData queries on relational entities to extract ETL data, supporting large batch processing, filtering, column selection, and pagination for data warehouse loading.

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
topNoMaximum records to return (default: 1000, max: 50000 for ETL)
skipNoNumber of records to skip for pagination
filterNoOData $filter. Operators: eq ne gt ge lt le, and/or/not, (). Partial text matching: startswith(Field,'v'), endswith(Field,'v'), contains(Field,'v') -- text columns only. Values must be single-quoted and are CASE-SENSITIVE ('us' does not match 'US'). A value containing a single quote cannot be filtered on at all. Example: startswith(Product,'TV') and Country eq 'US'. Assets whose lineage includes federated sources accept only eq/and/or/().
selectNoComma-separated column list for $select (e.g., "customer_id,amount,date")
orderbyNoOData $orderby expression (e.g., "amount desc, date asc")
asset_idYesAsset identifier - same as used in list_relational_entities (e.g., 'SAP_SC_FI_AM_FINTRANSACTIONS')
space_idYesSpace identifier (e.g., 'SAP_CONTENT')
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.
Behavior3/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It mentions large batch processing (up to 50,000 records), filtering, column selection, and pagination, but does not disclose side effects, permissions, rate limits, or error behavior. The read-only nature is implied but not stated explicitly.

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?

The description is concise—four sentences that front-load the core purpose and key capabilities. Every sentence provides useful information: what it does, batch limit, feature set, usage context, and a prerequisite tip. There is no redundancy or fluff.

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

Completeness4/5

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

Given the tool has 8 parameters and no output schema, the description competently covers the main usage context, capabilities, and a prerequisite step. It lacks explicit details about return value structure, but the absence of an output schema and the straightforward 'query' semantics mitigate this. The description is reasonably complete for a developer to start using the tool effectively.

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 input schema covers 100% of parameters with detailed descriptions, so the baseline is 3. The description adds no significant parameter-level detail beyond what the schema already provides; it only refers generally to filtering, column selection, and pagination, which are already documented in the schema.

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?

The description begins with a specific verb phrase 'Execute OData queries' and clearly identifies the resource as 'relational entities', immediately distinguishing this tool from analytical query tools. It further specifies the ETL context and explicitly references sibling tool list_relational_entities, making the purpose unmistakable.

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?

The description gives clear context for when to use this tool (ETL data extraction, data warehouse loading, analytics pipelines) and instructs users to call list_relational_entities first. However, it does not explicitly state when not to use it or name alternative query tools, leaving some ambiguity versus query_analytical_data or execute_query.

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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