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

Annotations are minimal (only openWorldHint: true), so the description carries the transparency burden. It does add value by enumerating the five in-scope metrics and three exclusion categories, and it surfaces the periodic-change semantics of results (周期变化). However, it doesn't explain the implication of the openWorldHint annotation (e.g., whether absent data means zero or unknown), nor any rate-limit or return-semantics behavior. Reasonable but not deep for a data-query tool.

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?

Well-structured and front-loaded: purpose, explicit non-goals, a bulleted metric list, an exclusion list, and concrete examples. Every section earns its place, and the metric enumeration helps an agent match user queries to this tool's capability without being bloated. The pricing block is machine-parseable meta-info appended, not filler.

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?

The tool is relatively complex (five distinct metrics, multiple exclusions, cross-language input), and the description covers all of it: what metrics are available, what's excluded, and realistic query phrasings. An output schema exists so return values needn't be documented. Could briefly mention open-world/empty-result semantics, but overall it is complete for an agent to select and 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?

Schema description coverage is 100% with both parameters (company_name, country_name) well-documented with concrete examples ('比亚迪股份有限公司', 'Tesla, Inc.', '中国', 'Japan'). The description adds value beyond the schema by showing full typical query formulations (e.g., '日本丰田自动车株式会社网络平台热度') and clarifying that full legal entity names are expected, reinforcing the level of specificity needed.

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 (查询/query) plus resource (reputation/brand periodic changes by specific company) and scope (media report popularity and official media browsing performance). It also names what it is NOT for (award reputation, non-negative proportion favorability), which explicitly distinguishes it from sibling enterprise_change_reputation_favorability. This is a specific verb+resource+scope with sibling differentiation.

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 explicitly states when to use (查询媒体研报热度及官方媒体访问浏览表现), what it's NOT for (不用于获奖口碑或非负面占比等美誉度评价), what's excluded (不包含：非本分类指标；按园区/产业链批量筛企业名单), and gives three typical query formulations. This is exemplary when/when-not guidance with clear boundary-setting against alternatives.

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