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SAP Datasphere MCP Server

SAP Datasphere MCP Server

by DimiDR
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

get_relational_entity_metadata

Retrieve detailed metadata for a relational entity, including column definitions, data types, SQL type mappings, and ETL extraction details, to support data warehouse loading and transformation.

Instructions

Get detailed metadata for a specific relational entity including column definitions, data types, SQL type mappings, and ETL extraction capabilities. Optimized for data warehouse loading and transformation workflows.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "space_id": "<space_id>",
      "asset_id": "<asset_id>"
    },
    "name": "get_relational_entity_metadata"
  }
}

Input Schema

NameRequiredDescriptionDefault
asset_idYesAsset/entity identifier (e.g., 'SAP_SC_FI_AM_FINTRANSACTIONS')
space_idYesSpace identifier (e.g., 'SAP_CONTENT')
include_sql_typesNoInclude SQL type mappings for target databases (default: true)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.4.0

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It states 'Get' (read-only) and enumerates the returned metadata types, which is useful. However, it does not disclose any limitations, authentication requirements, error behavior, or how include_sql_types affects the response, leaving some transparency gaps.

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 with no extraneous words. The core purpose is front-loaded, and the second sentence adds relevant context about the intended workflow. Every word earns its place.

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 no output schema, the description adequately describes return content (column definitions, data types, SQL type mappings, ETL capabilities). It does not cover error scenarios or pagination, but for a metadata retrieval tool with simple parameters, this is sufficient and complete enough for an agent.

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 coverage is 100% with detailed descriptions for all three parameters. The tool description does not add parameter-specific meaning beyond implying that the tool targets a specific entity (asset_id, space_id). Baseline 3 is appropriate since the schema handles parameter documentation.

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's function: 'Get detailed metadata for a specific relational entity' and enumerates the content (column definitions, data types, SQL type mappings, ETL extraction capabilities). This distinguishes it from siblings like get_relational_metadata (broader scope) and get_table_schema (narrower focus on table columns).

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 specifies the tool's niche: 'Optimized for data warehouse loading and transformation workflows,' which implies when to use it. However, it does not explicitly mention alternatives or cases where other metadata tools would be preferable, so it lacks explicit exclusions.

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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