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

No annotations are provided, so the description carries full burden. It transparently describes both default behavior (retrieval) and optional behavior (LLM integration), which are the key behavioral traits. No contradictions or omissions.

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?

Two concise sentences front-load the purpose and key behavioral distinction (default vs LLM mode). Every word adds value; no wasted space.

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?

The description adequately covers the tool's behavior given its low complexity. No output schema exists, but the description explains the two return types. All parameters are documented. It is complete for a straightforward Q&A tool.

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 coverage is 100% with descriptions for all parameters. The description adds extra meaning by explaining default behavior and the effect of 'synthesize', going beyond the schema's parameter descriptions.

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 answers insurance knowledge questions, distinguishing it from sibling tools like 'chat' which is more general. It specifies the default mode (knowledge base retrieval) and an optional LLM synthesis mode.

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 explains when to use the 'synthesize' parameter but does not explicitly provide when to use this tool versus alternatives like 'chat' or 'search_products'. Usage context is implied but not fully delineated.

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