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

Beyond the readOnly/idempotent annotations, the description discloses that it uses the same search engine as the official page (https://0523.tw/search) and returns results identical to the page. It details the output structure, sorting by relevance, and the behavior when confidence is low, providing substantial operational transparency.

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 concise yet informative, front-loading the main purpose, then covering source, output, usage tips, and sibling tools without redundancy. Every sentence contributes actionable information, making it well-structured and efficient.

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?

Even with an output schema present, the description thoroughly covers the tool's purpose, return fields, usage context, limitations, and related tools. It also explains the underlying data source and provides guidance for handling ambiguous queries, making it complete for an AI agent to select and invoke the tool 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?

The schema already covers 100% of parameters with descriptions, so the baseline is 3. The description adds value by advising that greater product_name specificity (brand/model/material/use) improves accuracy and by noting that the limit parameter caps at 5. This extra guidance exceeds what the schema provides, but not overwhelmingly so.

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 function: '用商品名稱／描述查台灣 CCC 稅則號別候選' (look up CCC tariff code candidates using product name/description). It specifies the output (up to 5 candidates with full code, product name, tariff rate, and regulations) and differentiates from sibling tools by referencing lucky_ccc_tariff_lookup and lucky_import_tax_estimate.

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?

Explicit guidance is provided: it says to use lucky_ccc_tariff_lookup for full details and lucky_import_tax_estimate for tax estimation after obtaining the CCC code. It also advises adding more specific details if no high-confidence candidate is found, covering both when-to-use and alternatives.

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