search_knowledge

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

No annotations are provided, so the description carries the full behavioral burden. It indicates a search/discovery operation and a routing relationship to get_table_knowledge, but it does not disclose the result shape, whether multiple matches are returned, how top_k affects output, or any limitations.

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 with no filler. The action verb is front-loaded, and the arrow to get_table_knowledge is a compact, valuable routing instruction that earns its place.

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 tool with no annotations and no output schema, the description is too thin. It leaves gaps around result semantics, top_k behavior, and usage context, and the close sibling search_knowledge_base is not distinguished. An agent would need additional inference to call it confidently.

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 description coverage is 0%, so the description must compensate. It adds meaning for query by specifying it is a natural-language use-case question, but top_k is completely unexplained, and there is no information about ranking, filtering, or expected output.

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 a specific verb ('Find') and names a clear resource: 'which table/family fits a use-case NL question.' It also points to get_table_knowledge as the natural follow-up, which helps orient the agent. However, it does not explicitly contrast itself with similarly named siblings like search_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?

There is no explicit when-to-use or when-not-to-use guidance. The phrase 'use-case NL question' implies a natural-language discovery use case, and the arrow to get_table_knowledge suggests a routing pattern, but no alternatives or exclusions among the many sibling tools are given.

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