Search tables by name pattern

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds valuable behavioral details: it uses SYS.TABLES and caps results at 2000, which are not conveyed by annotations. This exceeds the baseline for annotation coverage.

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?

Three concise sentences, front-loaded with the primary action. Each sentence provides distinct value: purpose, mechanics, and result cap. No wasted words.

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 search tool with 3 parameters and no output schema, the description covers the essential behavioral aspects: pattern matching, schema scope, source table, and result cap. It does not detail return format, but that is not critical for a search operation. Sibling differentiation is implied, though explicit alternatives would be more complete.

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?

Schema covers all 3 parameters with descriptions, but the description adds meaning: it clarifies that omitting schema_name searches all schemas, and that limit has a hard cap of 2000. This goes beyond just restating schema fields.

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 the purpose: finding tables via a LIKE pattern across all schemas or a specific one. It distinguishes itself from sibling tools like hana_list_tables (which lists all tables) and hana_search_columns (which searches columns), using specific verbs and resource scope.

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?

Provides clear context on when to use: when searching for tables by name pattern. It mentions the optional schema filter and the default all-schema behavior. However, it does not explicitly name alternatives or when not to use it, so it falls short of a 5.

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