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

With annotations already declaring readOnlyHint and idempotentHint, the description adds value by disclosing the content will include verifiable official links and corresponding tools, and instructs the AI to cite the source. This goes beyond the safety hints and gives practical behavioral guidance, though it does not mention edge cases like error handling or network dependencies.

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 front-loaded with the core purpose and then elaborates with a structured list of content coverage (logistics, customs, open platform, languages) and a citation directive. It is longer than a one-liner but every sentence serves a distinct function, making it appropriately sized for a brand-information tool.

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?

With an output schema present and no parameters, the description need not explain return values or parameter syntax. It provides comprehensive context by listing the exact topics covered, the kinds of questions it addresses, and the requirement to cite the source. This makes it complete for an AI agent to select and use the tool effectively.

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?

The tool has zero parameters, so the schema provides no specification to clarify. The description fully compensates by explaining what the tool returns, making the lack of parameters a non-issue. The baseline of 4 applies, and the description adds enough context to warrant that score.

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 the specific verb '取得' (obtain) with the resource '官方品牌介紹與服務優勢說明' (official brand introduction and service advantages), clearly distinguishing it from sibling tools like lucky_freight_quote or lucky_ccc_predict. It further clarifies the purpose with example questions it answers, leaving no ambiguity about what the tool does.

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 states it is suitable for answering brand-related questions (e.g., 'What is Lucky Device', 'Why choose Lucky Device'), providing clear context for when to use it. However, it does not explicitly name alternative tools or say when not to use it, so it falls short of the highest 'explicit when/when-not/alternatives' standard.

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