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

Annotations only include 'openWorldHint: true', which is minimal. The description adds valuable context: it queries 'periodic changes in business activities' and checks specific policy event impacts, with examples of typical questions. It does not mention rate limits, authentication, or error behaviors, but the description gives a clear picture of the tool's behavior and output semantics without contradicting 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 well-structured with a lead sentence, a bulleted list of included indicators, explicit exclusions, and typical question examples. While moderately long, every section serves a purpose, and the structure aids scanning. Slightly verbose but not wasteful.

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 tool with only two parameters and an existing output schema, the description is highly complete. It covers the scope, provides multiple usage examples, and clearly delimits what it does and does not do. The inclusion of typical questions removes ambiguity about intent and expected input format, making it easy for an agent to invoke 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?

Both parameters are fully described in the schema with examples (e.g., '比亚迪股份有限公司', 'Tesla, Inc.'). The description further enriches semantics by showing how country_name and company_name combine in typical question formats (e.g., '中国比亚迪股份有限公司' vs '美国Tesla, Inc.'), clarifying the relationship between parameters. Since schema coverage is 100%, this additional clarification earns a 4.

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 whether an enterprise is affected by foreign trade policy events from the US, EU, Japan/Korea, Asia, and globally, based on enterprise name. It uses a specific verb and resource and differentiates from siblings by focusing on 'external policy impact' (外部政策影响) and explicitly excluding policy text queries or batch screening. This is distinct from the many other enterprise_change_* siblings.

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 when-to-use guidance via typical questions (e.g., 'Is Tesla affected by EU trade policy?') and explicit exclusions: not for policy text interpretation, not for batch screening by park/industry chain, and not for non-本分类指标. While it names no alternative tools, the exclusions effectively guide an agent away from misuse. It could be a 5 if it explicitly referenced sibling tools.

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