qic_search

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals a non-obvious behavior: it auto-discovers the first available assistant and searches only that assistant's knowledge base. This explains potential variability in results, which is valuable. However, it does not mention edge cases like failures when no assistant exists or error behavior.

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 two short sentences, front-loaded with the core action and target. The second sentence adds essential behavioral detail without redundancy. Every word contributes value, making it appropriately concise.

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 presence of an output schema covers return value structure, so that is not a gap. The description explains the core action and the auto-discovery trait, but omits details about max_results and does not address what happens if no assistant is available. While adequate for a simple search tool, these omissions leave the description slightly incomplete.

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?

The schema description coverage is 0%, so the description must compensate for explaining parameters. It implicitly clarifies 'query' by mentioning the search action, but it says nothing about 'max_results', including its effect on the number of results or any constraints. The agent must infer max_results semantics solely from its name, which is a notable gap.

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 uses the specific verb 'Search' with a distinct resource, 'Amazon Q in Connect knowledge base', and explicitly lists common aliases ('QiC', 'Q in Connect'). This clearly differentiates the tool from generic sibling search tools like ai_search_content by narrowing scope to Amazon Q's knowledge base.

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 states clearly when to use the tool: for 'QiC', 'Q in Connect', or knowledge base searches. It does not explicitly name alternative tools or exclusions, but the context is sufficient for an agent to understand typical use cases. The auto-discovery note hints at limitations but lacks explicit 'when not to use' guidance.

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