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

With only openWorldHint as annotation, the description carries the transparency burden. It discloses that the tool returns a merged text of total/production/sales/dependent counts and explicitly states what is not included, such as other business classifications and company-name details. This adds behavioral detail beyond the annotation.

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 core purpose is front-loaded and clear, but the description is somewhat redundant: the metric list repeats the already-mentioned total/production/sales/dependent breakdown, and the Pricing block adds noise without behavioral value. Three example questions are helpful but slightly over-verbose.

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 the moderate complexity of a 3-parameter count tool with an output schema, the description adequately covers input semantics, output categories, exclusions, and example usage. It does not explicitly address alternatives, but the scope and typical questions are sufficient for an agent to select and invoke this tool correctly.

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%, but the description adds useful semantic context: 'year' is tied to the '近两年' (past two years) lookback window, and 'region' is clarified as country/province/city/district granularity. Typical questions further illustrate how the parameters combine in real queries.

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 uses a specific verb and resource: it is a query for the count of companies with outward investment in the past two years, filtered by concrete region and industrial-chain name. It also explicitly excludes company-list details, which differentiates it from its sibling tool chain_invest_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?

The description provides clear scope, exclusions, and typical question phrasings such as '2024年全国集成电路近两年有对外投资的企业有多少'. However, it does not explicitly state when to prefer this tool over alternatives like chain_invest_company_list or chain_invested_company_num; usage is implied rather than explicitly contrasted.

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