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MarioDeFelipe

SAP Datasphere MCP Server

get_space_info

Retrieve comprehensive metadata, table lists, schemas, and row counts for any SAP Datasphere space to verify configuration and explore contents.

Instructions

Get comprehensive information about a specific SAP Datasphere space.

Use this tool when:

  • User asks about a specific space (e.g., "Tell me about SALES_ANALYTICS")

  • You need to see what tables/views exist in a space

  • Checking space configuration and metadata

  • Following up from list_spaces() results

What you'll get:

  • Complete space metadata (status, owner, created date)

  • List of all tables and views in the space

  • Table schemas and row counts

  • Connection information

Required parameter:

  • space_id: Must be uppercase (e.g., 'SALES_ANALYTICS', 'FINANCE_DWH')

Example queries:

  • "Show me the SALES_ANALYTICS space"

  • "What tables are in FINANCE_DWH?"

  • "Tell me about the HR_ANALYTICS space"

Error handling:

  • If space not found, list_spaces() will show available spaces

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
space_idYesThe space ID in UPPERCASE format (e.g., 'SALES_ANALYTICS', 'FINANCE_DWH', 'HR_ANALYTICS'). Must match exactly.
Behavior4/5

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

No annotations provided, but description clearly implies a read-only operation ('Get comprehensive information') and lists what will be returned. It adds context like uppercase requirement for space_id. Could be more explicit about being read-only or lack of 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?

Well-structured with sections for usage, output, parameters, examples, and error handling. Every sentence is meaningful and adds value. No unnecessary text.

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?

For a simple tool with one parameter and no output schema, the description covers all necessary aspects: purpose, when to use, parameter details, example queries, and error handling. It is complete.

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 schema already documents the parameter. Description adds value by specifying the uppercase requirement and providing example queries, though it largely restates the schema description.

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 'Get comprehensive information about a specific SAP Datasphere space' and lists specific outputs (metadata, tables/views, schemas). It clearly distinguishes from siblings like list_spaces by specifying it targets 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 Guidelines5/5

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

Provides explicit 'Use this tool when:' section with concrete scenarios (user asks about a space, need to see tables/views, follow-up from list_spaces). Also gives error handling guidance (if space not found, use list_spaces).

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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