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

Given the annotations already declare readOnlyHint=true and openWorldHint=true, the description need not repeat safety traits. It adds useful context: the tool returns a 'structured, audited deliverable' and that inputs are validated server-side, implying a defined output format and error handling. No contradictions with annotations.

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 somewhat lengthy due to multiple example queries, but each example adds value by illustrating different use cases. The opening sentence is clear and the structure is logical. It could be tightened, but it is not bloated with meaningless filler.

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?

This is a complex tool covering multiple classification standards, and there is no output schema. The description gives many usage examples and states that the deliverable is structured and audited, but it does not describe the output format, confidence levels, or hierarchy beyond mentioning them. This is a gap given the tool's complexity, but the examples provide some context.

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 only 17%, meaning most parameters lack descriptions. The tool description does not compensate sufficiently; it mentions 'company description' in examples but fails to explain critical fields like focus_classifications, primary_revenue_source, company_name, company_url, or the async flag. This leaves the agent uncertain about how to populate these parameters correctly.

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 identifies the tool as an industry classifier for NAICS/SIC/NACE, and further extends to GICS, ISIC, and HS. It provides multiple example queries that specify exactly what the tool can do, such as returning NAICS codes, combined classifications, or segmenting lists. This distinguishes it from any sibling tools by focusing on industry classification specifics.

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 many contextual usage examples, such as 'For EU procurement compliance' and 'for equity analysis', which implicitly tell the agent when to use this tool. It lacks explicit 'when not to use' or alternative tool suggestions, but the rich examples effectively convey the intended scenarios. The note about server-side validation also offers guidance.

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