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

The annotation openWorldHint=true already communicates that results may be incomplete, so the description doesn't need to echo that. However, the description does not add much behavioral context beyond the filter logic, such as pagination behavior, exact return format, or rate limits. Since the annotation carries the open-world disclosure, a score of 3 is fair—the description adds some context (e.g., returns 'these industrial/commercial data items') but not deep behavioral details.

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 a single, moderately long paragraph, but it is well-structured and front-loads the primary purpose. It includes examples and exclusions without being excessively verbose. While not extremely concise, it packs essential information efficiently and is easy to scan.

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 covers the key usage scenarios, exclusions, and typical queries, and the input schema plus an existing output schema provide comprehensive parameter and result documentation. For a list-query tool with moderate complexity, the description is complete enough for an agent to select and invoke it correctly without further clarification.

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%, so the baseline is 3. The description adds value by explaining that input_text is the main query containing mandatory business conditions and optional administrative region, and clarifies that capital_wan can be a number (default >=) or an object with explicit operator. It also provides concrete examples that illustrate parameter usage, which is more than just restating the schema.

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 purpose: to query company lists based on filter conditions and return relevant industrial/commercial data. It distinguishes from sibling tools by explicitly stating that it is not for looking up a known company by name nor for aggregate regional totals, and it names the appropriate alternatives (gov_data_enterprise_scale / enterprise_change). Typical query examples further clarify the intended scope.

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 explicit usage guidance: it lists two filter categories (administrative region optional, business conditions required), gives specific examples of valid queries, and clearly identifies exclusion cases (known company lookup, aggregate totals) with pointers to the correct tools. This is highly actionable for an agent deciding when to use this tool.

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