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

The annotations only provide openWorldHint, so the description carries most of the behavioral disclosure burden. It explains that the output merges total/production/sales/dependent list text and states what is not included. No contradiction with annotations is present.

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 well-structured with a purpose line, covered types, exclusions, and typical questions, followed by pricing. There is some redundancy in repeating the four list categories, but overall it is efficient and front-loaded.

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 simple 3-parameter list tool with an output schema, the description is reasonably complete: it covers usage context, input semantics, and output boundaries. It does not specify behavior when year is omitted, but the schema marks it optional and the examples are sufficient.

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 100%, so the baseline is 3. The description adds useful meaning by specifying region granularity (country, province, city, district) and giving concrete examples that map to year, region, and chain_name, going beyond the schema's brief examples.

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 it queries a science-tech SME list by specific region and industry chain, and explicitly lists the four returned categories (total, production, sales, dependent). It also distinguishes itself from count-only sibling tools by stating '仅返回数量不返回名单' is not included.

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?

Typical questions such as '2024年全国集成电路科技型中小型企业名单' make the intended usage concrete. The description gives an explicit exclusion ('不包含：其他企业分类的统计；仅返回数量不返回名单'), which tells the agent not to use this tool for count-only queries, though it does not name the sibling num tool explicitly.

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