stock_new_ipo_cninfo

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, so safety profile is covered. The description adds non-redundant behavioral details: the source URL (indicating the origin of data) and the return type (pandas.DataFrame). However, it does not disclose limitations, filtering behavior, or any operational caveats, so it adds only modest value beyond 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 compact and front-loaded with the data source, but the first line repeats the annotation title, which is redundant. The URL and return type lines are useful and non-repetitive. Overall, it is under-specified but not verbose; the redundancy costs it a perfect score.

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 description is insufficiently complete for a tool with no output schema. It only states that it returns a DataFrame of '新股发行' (new stock issuance), but does not explain what columns, time ranges, or specific data points are included. It also fails to differentiate from many IPO-related sibling tools, leaving the agent uncertain about the exact content and scope of the returned data.

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 schema description coverage is 100% (trivially). The baseline for no parameters is 4, and the description need not explain any input semantics. It correctly implies the tool takes no arguments and returns a complete dataset.

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 is essentially a restatement of the tool name/title: '巨潮资讯-数据中心-新股数据-新股发行' is identical to the annotation title. It uses a noun phrase ('新股发行') rather than a specific verb like 'retrieve' or 'list', and provides no differentiation from similar stock IPO tools like stock_ipo_summary_cninfo or stock_new_gh_cninfo. The URL and return type add slight context but do not clarify the action performed.

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 alternatives. The description only provides a source URL and return type, with no mention of scenarios, prerequisites, or exclusions. Unlike strong examples that explicitly name sibling tools for comparison, this description leaves the agent to infer usage context.

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