Skip to main content
Glama
MarioDeFelipe

SAP Datasphere MCP Server

by MarioDeFelipe
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

list_catalog_assets

Browse all data assets across SAP Datasphere spaces to discover tables, views, analytical models, and more. Filter by space, type, or exposure status with OData expressions.

Instructions

Browse all data assets across all SAP Datasphere spaces.

Use this tool when:

  • User asks "What assets are available in Datasphere?"

  • Building a complete data catalog or asset inventory

  • Discovering available data assets across all spaces

  • Searching for specific asset types across the system

  • Understanding the overall data landscape

What you'll get:

  • Asset IDs and names across all spaces

  • Asset types (AnalyticalModel, View, Table)

  • Space information for each asset

  • Consumption URLs (analytical and relational)

  • Exposure status and metadata URLs

  • Creation and modification timestamps

Available parameters:

  • select_fields: Specific fields to return (e.g., ['name', 'description', 'spaceId'])

  • filter_expression: OData filter (e.g., "spaceId eq 'SAP_CONTENT'")

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

  • skip: Results to skip for pagination

  • include_count: Include total count of assets

Example queries:

  • "List all available assets in Datasphere"

  • "Show me all analytical models across all spaces"

  • "Find assets in the SAP_CONTENT space"

  • "List the first 20 assets with their consumption URLs"

Common filters:

  • By space: filter_expression="spaceId eq 'SAP_CONTENT'"

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

  • Exposed only: filter_expression="exposedForConsumption eq true"

  • Combined: filter_expression="spaceId eq 'SALES' and assetType eq 'View'"

Asset types you'll see:

  • AnalyticalModel: Multi-dimensional models for analytics

  • View: SQL views combining multiple data sources

  • Table: Physical tables with business data

  • Fact: Fact tables in analytical models

  • Dimension: Dimension tables in analytical models

Note: This uses the Catalog API: GET /api/v1/datasphere/consumption/catalog/assets

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
select_fieldsNoSpecific fields to return (e.g., ['name', 'description', 'spaceId']). If not specified, returns all fields.
filter_expressionNoOData filter expression (e.g., "spaceId eq 'SAP_CONTENT'" or "assetType eq 'AnalyticalModel'").
topNoMaximum number of results to return (default: 50, max: 1000).
skipNoNumber of results to skip for pagination (default: 0).
include_countNoInclude total count of matching assets (default: false).

Tool Definition Quality

A4.7/5.0
Behavior4/5

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

With no annotations, the description carries full burden. It states the tool uses a GET API (read-only) and lists what will be returned. It does not explicitly mention side effects, but 'browse' implies non-destructive. Adds context like asset types and fields returned, but could note limitations or auth needs.

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 well-structured with sections, bullet points, and examples. It front-loads the main purpose. However, it is somewhat lengthy and repeats asset types (once in a list and again later). Minor redundancy prevents a 5.

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?

Despite lacking an output schema, the description compensates by detailing what will be returned (IDs, names, types, URLs, timestamps). It also covers usage scenarios, common filters, and provides example queries, making it complete for an asset listing tool.

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

Parameters5/5

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 by explaining each parameter with examples, defaults, and common filter expressions, such as default top 50, max 1000, and sample OData filters.

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 explicitly states 'Browse all data assets across all SAP Datasphere spaces.' The verb 'browse/list', resource 'data assets', and scope 'all spaces' are clear. It distinguishes from siblings like 'get_space_assets' (space-specific) and 'search_catalog' (search vs list).

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

Usage Guidelines5/5

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

The description provides explicit when-to-use scenarios such as 'User asks "What assets are available in Datasphere?"' and 'Building a complete data catalog'. It includes example queries and common filters, giving clear context for usage.

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