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MarioDeFelipe

SAP Datasphere MCP Server

search_tables

Search for tables and views by name or description across all Datasphere spaces. Use keywords to find tables even with partial matches.

Instructions

Search for tables and views across all Datasphere spaces by name or description.

Use this tool when:

  • User asks "Find tables with customer data"

  • Looking for tables containing specific keywords

  • Don't know exact table name but know the domain

  • Searching across multiple spaces

Search behavior:

  • Searches both table names and descriptions

  • Case-insensitive matching

  • Returns results from all spaces (or specific space if filtered)

  • Includes table metadata (type, columns, row counts)

Search tips:

  • Use domain keywords: "customer", "sales", "order", "finance"

  • Partial matches work: "cust" finds "CUSTOMER_DATA"

  • Filter by space_id to narrow results

Example queries:

  • "Find all tables related to customers"

  • "Search for sales order tables"

  • "Show me all tables with 'finance' in the name"

Next steps:

  • Use get_table_schema() for detailed column information

  • Use execute_query() to retrieve actual data

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
search_termYesKeyword to search for in table names and descriptions (e.g., 'customer', 'sales', 'order'). Case-insensitive, partial matches work.
space_idNoOptional: Filter results to a specific space (e.g., 'SALES_ANALYTICS'). Leave empty to search all spaces.
Behavior3/5

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

Without annotations, the description carries full burden. It explains search behavior (case-insensitive, across spaces, includes metadata), but omits limit on results, sorting, or performance characteristics.

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?

Well-structured with sections (use cases, behavior, tips, examples). Slightly lengthy but every section adds value. Front-loaded with core purpose.

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

Completeness4/5

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

Given no output schema, description explains return includes metadata. Provides next steps for further actions. Sufficient for agent to understand usage.

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% with good descriptions. The description adds search tips, partial match behavior, and example queries, enhancing 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 it searches tables and views by name or description across all Datasphere spaces. It provides specific examples and distinguishes from siblings like search_catalog.

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?

Explicit when-to-use list with concrete user queries (e.g., 'Find tables with customer data'). Includes next steps and search tips, but lacks explicit when-not-to-use or comparison with siblings.

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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