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

Annotations only include openWorldHint, so the description carries the burden for behavioral disclosure. It specifies that it returns counts for specific metrics, but does not mention rate limits, authentication, or behavior on invalid inputs. It doesn't contradict annotations, but the information is limited given the sparse 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 clear opening statement, a bullet-like list of metrics, exclusions, and typical questions. It uses paragraphs efficiently without redundancy. While it is somewhat long, each section adds value, making it acceptable for a tool with many specific metrics.

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 that the tool has an output schema (as indicated by context), the description does not need to explain return values. It covers the key aspects: purpose, included metrics, exclusions, and examples. It is sufficiently complete for an agent to decide when to use it and what to expect, given the defined schema.

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% for both parameters (company_name, country_name). The description adds examples and clarifies the format (e.g., '中国比亚迪股份有限公司'), but does not go beyond the schema's inherent semantics. Baseline 3 is appropriate since the schema already explains the parameters well.

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 it queries innovation outcomes (patents, software copyrights) for a specific company, enumerates the exact metrics covered (valid patents, invention patents, utility models, design patents, copyrights), and distinguishes itself by explicitly excluding R&D input and online sales indicators. It also gives typical queries, making the purpose unambiguous.

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 explicit exclusions: '不用于研发投入、线上销售占比等创新力指标' and '不包含：非本分类指标；按园区/产业链批量筛企业名单'. It also lists typical question patterns, which clarifies when to use this tool. While it doesn't name alternative tools, the exclusions are strong enough to guide selection.

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