stock_ipo_hk_ths

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, establishing a safe, read-only operation. The description adds that the return type is a pandas.DataFrame and provides the data source URL, but it does not disclose any additional behavioral traits like data freshness, pagination, or network dependencies. This adds modest value beyond the 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 extremely concise, consisting of a title, source URL, return description, and return type. Each line serves a purpose, and it is front-loaded with the most important information. No redundant or filler content is present.

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?

For a simple, parameterless data retrieval tool, the description is sufficiently complete. It specifies the data source, the nature of the data (HK IPO subscription and allotment), and the return type. While it does not detail the DataFrame columns, the low complexity and supporting annotations make this acceptable.

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, and the input schema has 100% coverage (vacuously). With no parameters to document, the baseline of 4 applies, and the description correctly focuses on the return value rather than parameter details.

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 states this tool retrieves Hong Kong IPO subscription and allotment data from the Tonghuashun Data Center, with a specific URL and return type. It clearly identifies the resource (HK IPO data) and the source (THS), distinguishing it from sibling tools like stock_ipo_ths or stock_xgsr_ths. However, it lacks an explicit verb like '获取' or 'retrieves', relying on the context of returning a DataFrame.

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 implies the tool is for accessing HK IPO subscription and winning data, but it does not provide explicit guidance on when to use it versus alternatives such as stock_ipo_ths, stock_xgsr_ths, or other IPO data tools. No exclusions or alternative tool references are mentioned, leaving usage context only implied.

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