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

With no annotations, the description carries the full burden of behavioral disclosure. It explains the search matching behavior (against dataset codes, Chinese descriptions, and field names) and the taifex/ prefix convention for futures exchange datasets. This adds meaningful context beyond just 'search', though it doesn't mention return format or rate limits.

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 three concise sentences in Chinese, front-loaded with the core purpose, followed by a usage instruction and a useful domain hint. Every sentence adds value and there is no redundant or filler wording.

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 has 3 optional params, no annotations, and no output schema. The description establishes the tool's role in the workflow (finding dataset_id before fetching), its search scope, and a naming convention. It could mention the return structure, but the purpose implicitly promises dataset IDs in the results, making it sufficiently complete for an AI agent.

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%, so the baseline is 3. The description enriches the parameter semantics by explaining that query matches dataset codes, Chinese descriptions, and field names, and by providing a specific tag example for futures/options. This goes beyond the schema's brief parameter descriptions.

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 purpose: searching available datasets from Taiwan Stock Exchange and Futures Exchange OpenAPI, specifically to find dataset_id before retrieving data. It distinguishes itself from siblings like twse_get_dataset (which fetches data) and twse_describe_dataset (which describes a dataset).

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?

Provides explicit usage context: '取資料前先用這個找 dataset_id' (use this before fetching data to find dataset_id), and gives a concrete search hint for futures/options using tag='期貨與選擇權'. It does not explicitly name alternative tools or state when not to use it, but the workflow guidance is clear and actionable.

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