Search columns by name pattern

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

Annotations already declare this as a read-only, idempotent operation, and the description adds the underlying system view (SYS.TABLE_COLUMNS) and a hard result cap of 1000. These are meaningful, non-obvious behavioral details that go beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is just three concise clauses that front-load the action and include key constraints (LIKE pattern, schema filter, 1000 cap) with zero wasted words. It is ideal in length and structure.

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

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

For a simple search tool with good annotation coverage, the description covers the source and the result cap. The lack of an output schema means the description could optionally clarify what fields are returned, but this is a minor gap given the tool's straightforward purpose.

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

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All three parameters are fully documented in the schema (100% coverage), so the description adds no additional parameter-level detail. The description's scope phrasing aligns with the schema, but does not go beyond the baseline expected from well-documented schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific action ('Find all columns matching a LIKE pattern') with a defined resource scope ('across all tables or within a schema'), distinguishing it from sibling search tools like hana_search_tables. Including the data source and result cap adds further clarity.

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

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

The description implies clear usage context via the LIKE pattern and the optional schema filter, but it does not explicitly name alternative tools or exclusions. It provides enough context for an agent to choose it over similar list/describe tools, though it lacks direct comparison.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.