Skip to main content
Glama
MarioDeFelipe

SAP Datasphere MCP Server

by MarioDeFelipe
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

get_space_assets

Retrieve all assets within a specified SAP Datasphere space, including tables, views, and analytical models. Filter results by type or exposure status for precise asset discovery.

Instructions

List all data assets within a specific SAP Datasphere space.

Use this tool when:

  • User asks "What assets are in the SAP_CONTENT space?"

  • Browsing assets within a specific space

  • Creating a space-specific asset inventory

  • Filtering assets by type within a space

  • Validating space contents and available data

  • Understanding what data is available in a space

What you'll get:

  • All assets within the specified space

  • Asset names, descriptions, and types

  • Exposure status for each asset

  • Consumption URLs (analytical and relational)

  • Creation and modification timestamps

  • Asset counts and pagination info

Required parameters:

  • space_id: The space to browse (e.g., 'SAP_CONTENT')

Optional parameters:

  • filter_expression: Filter by asset type or other criteria

  • top: Maximum results (default 50, max 1000)

  • skip: Results to skip for pagination

Example queries:

  • "List all assets in the SAP_CONTENT space"

  • "Show me analytical models in SALES_ANALYTICS"

  • "What tables are available in FINANCE_SPACE?"

  • "List exposed assets in SAP_CONTENT"

Common filters:

  • By type: filter_expression="assetType eq 'AnalyticalModel'"

  • Exposed only: filter_expression="exposedForConsumption eq true"

  • By name pattern: filter_expression="contains(name, 'Financial')"

  • Combined: filter_expression="assetType eq 'View' and exposedForConsumption eq true"

Asset types:

  • AnalyticalModel: Multi-dimensional models with dimensions and measures

  • View: SQL views combining data from multiple sources

  • Table: Physical tables with business data

  • Fact: Fact tables in dimensional models

  • Dimension: Dimension tables for analysis

Use cases:

  • Space content discovery

  • Asset inventory generation

  • Data availability validation

  • Finding specific asset types

  • Understanding space data landscape

Note: This uses the Catalog API: GET /api/v1/datasphere/consumption/catalog/spaces('{spaceId}')/assets

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
space_idYesThe space ID in UPPERCASE format (e.g., 'SAP_CONTENT', 'SALES_ANALYTICS'). Must match exactly.
filter_expressionNoOData filter expression (e.g., "assetType eq 'AnalyticalModel'" or "exposedForConsumption eq true").
topNoMaximum number of results to return (default: 50, max: 1000).
skipNoNumber of results to skip for pagination (default: 0).

Tool Definition Quality

A4.4/5.0
Behavior4/5

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

Since no annotations are provided, the description carries the full burden. It details what the tool returns: asset names, descriptions, types, exposure status, consumption URLs, timestamps, counts, pagination. It also notes the underlying API call. It does not mention auth requirements or rate limits, but for a read-only listing tool, the transparency is high.

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

Conciseness4/5

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

The description is fairly long but well-structured into clear sections (use cases, parameters, examples, filters, asset types). Each section adds relevant information without redundancy. It is appropriately sized for the tool's complexity.

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

Completeness5/5

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

Given the 4 parameters, no output schema, and no annotations, the description is remarkably complete. It covers all parameter usage, provides practical examples, common OData filter patterns, and lists asset types. An agent can fully understand how to invoke the 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%, so baseline is 3. The description adds significant value with example values ('SAP_CONTENT'), default values for top and skip, common filter examples, and descriptions of asset types. This goes beyond the schema definitions.

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 'List all data assets within a specific SAP Datasphere space' with a specific verb and resource. It distinguishes from siblings like list_catalog_assets and search_catalog by focusing on assets within a single space.

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 explicitly lists user queries and scenarios that trigger the tool, such as 'What assets are in the SAP_CONTENT space?' and 'Filtering assets by type within a space.' However, it does not provide explicit when-not-to-use guidance or name alternative tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/MarioDeFelipe/sap-datasphere-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server