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

Beyond the openWorldHint annotation, the description discloses that the result is a merged text of total/production/sales/dependent counts, and that other categories and company lists are excluded. It does not explain optional-year fallback behavior, but for a read-style count query with an output schema, this is reasonably transparent.

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 compact and well-structured with labeled sections for metrics, exclusions, and example questions. The pricing block adds a small amount of non-behavioral clutter, but overall every sentence serves a clear purpose.

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 count tool with an output schema, the description adequately covers purpose, parameter usage, and exclusions. It is well suited to disambiguate from many sibling tools. A minor gap is not explaining what happens when the optional year is omitted.

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 descriptions already cover all parameters at 100%. The description adds example phrases and geographic granularity (国家/省份/城市/区县), but does not substantially extend the semantics already present in the input 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?

Description clearly states it queries the count of deregistered companies for a specific region and industry chain, with a specific verb ('查询') and resource. It also lists the exact metric types returned and explicitly excludes company list details, distinguishing it from sibling list tools like chain_close_company_list.

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?

Provides typical question examples and explicitly states what is not included ('不包含：其他企业分类的统计；企业名单明细'), which signals when not to choose this tool. However, it does not name an alternative tool explicitly, so it stops short of full when/when-not guidance.

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