stock_new_gh_cninfo

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

The annotations already declare readOnlyHint=true and destructiveHint=false, so the tool is known to be safe. The description adds only that it returns a pandas.DataFrame, but does not disclose any additional behavioral traits such as data scope, freshness, pagination, or network dependencies. For a read-only data retrieval tool, the description is minimal and provides little 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, with no unnecessary words. It includes the source URL and return type in a compact format. While it could be more structured (e.g., with headers), the brevity is appropriate for such a simple tool, and every line provides some informational value.

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 no output schema, so the description must clarify what data is returned. It only says '新股过会' (IPO approval) and the return type, without describing columns, examples, or the nature of the data. This is inadequate for an agent to understand the tool's output or to choose it over closely related tools. The absence of any details beyond the label makes the description incomplete.

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 is empty. According to the rubric, a zero-parameter tool gets a baseline of 4, and the description correctly does not attempt to explain nonexistent parameters. No additional parameter semantics are needed.

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 the tool fetches new stock data related to '新股过会' (IPO approval) from cninfo's data center, with a source URL. This gives a specific resource and a clear domain (new stock listings passing review), distinguishing it from sibling tools like stock_new_ipo_cninfo or stock_ipo_review_em. However, it's in Chinese and somewhat terse, lacking an explicit verb like 'get' or 'return', though the return type is mentioned.

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?

There is no guidance on when to use this tool versus sibling tools such as stock_new_ipo_cninfo or stock_ipo_summary_cninfo. The description merely labels the dataset and provides a URL; it does not explain the specific use case or any exclusions. This leaves the agent to infer from the name alone, which is insufficient given many similar tools.

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