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MarioDeFelipe

SAP Datasphere MCP Server

find_assets_by_column

Search across spaces to find tables/views containing a specific column name for data lineage and impact analysis.

Instructions

Find all assets (tables/views) containing a specific column name across SAP Datasphere spaces.

Use this tool when:

  • User asks "Which tables contain CUSTOMER_ID?"

  • Performing data lineage analysis

  • Impact analysis before schema changes

  • Finding datasets for specific use cases

  • Locating related data across spaces

What you'll get:

  • Asset names and types (View, Table, etc.)

  • Space IDs where assets are located

  • Column information (name, type, position)

  • Total column count per asset

  • Consumption URLs for data access

Use cases:

  • Data lineage discovery (find all uses of a column)

  • Impact analysis (before renaming/removing columns)

  • Dataset discovery (find tables with specific fields)

  • Cross-space data exploration

  • Schema relationship mapping

Example queries:

  • "Find all tables with CUSTOMER_ID column"

  • "Which views contain SALES_AMOUNT?"

  • "Show me assets with COUNTRY_CODE in SAP_CONTENT space"

  • "List tables that have ORDER_DATE column"

Performance notes:

  • Searches across multiple spaces by default

  • Uses intelligent caching for better performance

  • Results limited to 50 assets by default (configurable)

  • Case-insensitive search by default

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
column_nameYesColumn name to search for (case-insensitive by default). Examples: 'CUSTOMER_ID', 'SALES_AMOUNT', 'ORDER_DATE'
space_idNoOptional: Limit search to specific space (e.g., 'SAP_CONTENT'). Leave empty to search all spaces.
max_assetsNoOptional: Maximum number of matching assets to return (1-200). Default: 50
case_sensitiveNoOptional: Perform case-sensitive column name matching. Default: false
Behavior5/5

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

With no annotations, the description fully covers behavior: search across spaces, caching, default limits, case-insensitivity. It also details what the agent will receive in results, ensuring no surprises.

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 and bullets, and the first sentence clearly states the purpose. However, some repetition exists (e.g., 'Use cases' overlaps with 'Use this tool when'), making it slightly longer than necessary.

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 4 parameters and no output schema, the description adequately explains what is returned (asset names, types, space IDs, column info, etc.) and includes performance notes. Covers all essential context 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%, so baseline is 3. The description adds usage examples but no new semantic info beyond the schema's parameter descriptions. Minimal added value.

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 verb 'find' and the resource 'assets containing a specific column name across SAP Datasphere spaces.' This is distinct from sibling tools like search_catalog or search_tables, which are broader. No ambiguity.

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 'Use this tool when' section lists specific queries and use cases (data lineage, impact analysis). It provides explicit context but does not mention when not to use or alternatives, so it's slightly below the top tier.

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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