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MarioDeFelipe

SAP Datasphere MCP Server

get_table_schema

Retrieve column names, data types, primary keys, and metadata for any table or view to understand its structure before querying.

Instructions

Get detailed schema information for a specific table or view.

Use this tool when:

  • User asks "What columns are in CUSTOMER_DATA?"

  • Need to understand table structure before querying

  • Planning JOIN operations (need to see key columns)

  • Checking data types for analysis

What you'll get:

  • Complete column list with data types

  • Primary key indicators

  • Column descriptions

  • Table metadata (row count, last updated)

Required parameters:

  • space_id: The space containing the table (uppercase)

  • table_name: Exact table name (case-sensitive, usually uppercase)

Example queries:

  • "Show me the schema of CUSTOMER_DATA in SALES_ANALYTICS"

  • "What columns does SALES_ORDERS have?"

  • "Describe the GL_ACCOUNTS table structure"

Best practices:

  • Use search_tables() first if you don't know the exact table name

  • Check column types before writing queries

  • Identify key columns for JOINs

Next steps:

  • Use execute_query() with proper column names and types

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
space_idYesThe space ID containing the table (e.g., 'SALES_ANALYTICS'). Must be uppercase.
table_nameYesExact table or view name (e.g., 'CUSTOMER_DATA', 'SALES_ORDERS'). Case-sensitive, typically uppercase.
Behavior4/5

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

With no annotations, the description fully explains the output: column list, data types, primary keys, column descriptions, and table metadata. It doesn't mention permissions or rate limits, but for a read-only schema tool, the behavioral disclosure is sufficient and accurate.

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 clear sections (use cases, what you'll get, required parameters, examples, best practices). It is front-loaded with the core purpose. Though somewhat lengthy, every section contributes useful context without redundancy.

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 details the output contents and provides a complete usage flow: prerequisites (search_tables), inputs, and next steps (execute_query). It addresses all common use cases and integrates well with sibling tools.

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% and the description largely repeats parameter information (case-sensitivity, uppercase). It adds value through examples (e.g., 'SALES_ANALYTICS') and context, but the baseline for high coverage is 3, and the description doesn't substantially enhance understanding beyond the schema.

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 retrieves schema information for a table or view. It differentiates from siblings by explicitly mentioning using search_tables() if the table name is unknown, and contrasts with execute_query() for querying. Examples reinforce the specific use case.

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 'Use this tool when' section lists concrete scenarios like checking columns or planning JOINs. Best practices advise using search_tables() first if uncertain, and Next steps suggest execute_query() after schema retrieval. This provides clear guidance on when to use and what alternatives exist.

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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