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

With no annotations provided, the description takes on the full burden of behavioral disclosure. It states that the output is a 'compact list capped at 25 with a total match count,' which gives concrete expectations about result limits and return content. It also implies read-only search behavior. However, it does not mention what happens when no filters are supplied (e.g., whether all companies are returned) or ordering, leaving some gaps.

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 sentences long and immediately front-loads the purpose. The first sentence covers the resource and filters, and the second sentence covers the return behavior and a necessary prerequisite. Every word contributes value with no redundancy.

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?

Given that there is no output schema, the description provides a reasonable summary of the return value ('compact list capped at 25 with a total match count'). It also explains the dataset scope and how to obtain valid segment IDs. It could be more explicit about the exact fields in the returned list, but overall it covers the essential information for a search 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?

The schema description coverage is 100%, with each parameter (query, country, segment) already having a clear description. The tool description adds no additional parameter-specific semantics beyond what the schema provides, so the baseline score of 3 is appropriate.

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's action ('Search') and resource ('wafergraph's semiconductor & AI supply-chain company dataset'), and specifies the search dimensions (name/one_liner substring, segment, country). It also notes the dataset size (615 companies across 12 segments). However, it does not explicitly differentiate itself from sibling tools like filter_companies or get_company, so it stops short of a 5.

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 provides a clear usage context: searching by name/one_liner, segment, and/or country. It explicitly recommends using get_segments first when segment IDs are unknown, which is a direct usage guideline. It does not explicitly state when not to use this tool, but the prerequisite and context make the intended usage clear.

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