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MarioDeFelipe

SAP Datasphere MCP Server

query_analytical_data

Execute OData queries on analytical models to retrieve aggregated data with dimensions and measures for business intelligence and reporting.

Instructions

Execute OData queries on analytical models to retrieve aggregated data with dimensions and measures. Supports full OData query syntax: $select (column selection), $filter (WHERE conditions), $orderby (sorting), $top/$skip (pagination), $apply (aggregations with sum/average/min/max/count/groupby). Perfect for business intelligence, reporting, and data analysis.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
space_idYesSpace identifier
asset_idYesAsset identifier
entity_setYesEntity set name to query
selectNoComma-separated list of dimensions/measures to return (OData $select)
filterNoOData filter expression (e.g., 'Amount gt 1000 and Currency eq "USD"')
orderbyNoSort order (e.g., 'Amount desc, TransactionDate asc')
topNoMaximum number of results (default: 50, max: 10000)
skipNoNumber of results to skip for pagination
countNoInclude total count in response
applyNoAggregation transformations (e.g., 'groupby((Currency), aggregate(Amount with sum as TotalAmount))')
Behavior2/5

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

No annotations provided, so description carries full burden. It only lists supported OData syntax and parameters but does not disclose behavioral traits like read-only nature, performance implications, or 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?

The description is two sentences, front-loaded with purpose and enriched with details. Every sentence adds value without redundancy.

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 10 parameters and no output schema, the description adequately covers the tool's purpose and supported syntax. It could benefit from more on response format or error handling but is sufficient for a complex query tool.

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?

Schema description coverage is 100%, so baseline 3. The description adds context by listing OData query syntax and examples, but does not provide significantly deeper meaning beyond what the schema already offers.

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 clearly states the tool executes OData queries on analytical models to retrieve aggregated data with dimensions and measures, and lists specific OData syntax. It distinguishes from sibling tools like query_relational_entity and execute_query by focusing on analytical models and OData.

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

Usage Guidelines3/5

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

The description mentions it's 'Perfect for business intelligence, reporting, and data analysis,' implying usage context but does not explicitly state when not to use it or mention alternatives among siblings.

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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