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

With only openWorldHint: true present, the description carries the load and does so well by enumerating the three indicator categories included, spelling out exclusions (batch filtering by park/industry chain), and disclosing per-run pricing (30 credits). This gives the agent a solid sense of result scope. It doesn't explain the openWorldHint implications or describe the return envelope, but the presence of an output schema covers return-structure concerns.

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 clear sections: core purpose, included indicators, exclusions, and typical questions, followed by a compact pricing block. Each section serves a purpose and the most decision-relevant information (what it does/doesn't do) appears first. Slightly long overall, but every piece earns its place.

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 tool's simplicity (2 params, 100% schema coverage, output schema present), the description covers the essentials: purpose, scope, exclusions, examples, and pricing. There are no major gaps; the only minor omission is explicit discussion of what the returned data looks like or how openWorldHint affects results, but the output schema mitigates this.

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% with both parameters (company_name, country_name) already documented with examples. The description's typical questions reinforce parameter pairing (e.g., '中国比亚迪股份有限公司' → country=中国, company=比亚迪), which is mildly useful, but this doesn't add meaning beyond what the schema already provides.

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+resource structure, clearly stating the tool queries periodic changes in government-enterprise relations by company name, covering R&D funding, subsidies, tax preferences, and special policy support. It differentiates from sibling tools by explicitly stating what it does NOT do (query industry policy changes or government visits), and provides concrete example questions. This saves the agent from confusing it with nearby 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 gives explicit when/when-not guidance: it clearly delimits inclusion scope ('用于查询政府研发资助、补贴税收优惠及专项政策支持'), states exclusions ('不用于查询行业是否出台新政策，也不用于政府来访视察类互动', '不包含：非本分类指标'), and provides typical question formats (典型问法) for each included indicator. This is exactly the kind of explicit guidance the rubric asks for, only missing named sibling tools.

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