suggest_cautions

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

With no annotations, the description carries the full burden. It discloses that it searches 'all domain knowledge entries (catalog and extracted)' via affects_columns matching, and specifies the return dict with table_names, cautions, and count. This provides useful transparency for a read-only suggestion tool, though edge cases are not discussed.

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 well-structured with a purpose sentence, a behavior sentence, and clear Args/Returns sections. There is no wasted text; every sentence adds value.

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 one-parameter tool with an output schema, the description is complete: it explains the search behavior, the argument format, and the return structure. No critical aspects are missing for an agent to invoke it correctly.

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 describes table_names only as a string with no description. The description adds that it is a 'comma-separated string of table/source names', fully clarifying the expected format and meaning beyond the schema. This compensates for the 0% schema coverage.

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 tool 'suggests cautions for the given table/source names', a specific verb+resource. It further differentiates from siblings by describing the matching against affects_columns in domain knowledge entries, distinguishing it from generic search or knowledge retrieval tools.

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?

Usage context is implied: when you have table/source names and need cautions, this tool searches domain knowledge. However, there is no explicit mention of when not to use it or alternatives like search_catalog or get_domain_knowledge, so guidance is not fully developed.

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