stock_sy_jz_em

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is covered. The description adds the source URL and return type (pandas.DataFrame), but does not disclose potential pagination, rate limits, or date format validation. With strong annotations, the added behavioral context is minimal but not contradictory, making a score of 3 appropriate.

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, containing only a title line, a URL, and docstring-style param/return annotations. It is structured and scannable, with no redundant filler. It loses one point because the title is repeated verbatim in the annotations, adding slight duplication without new information.

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 single-parameter read-only data retrieval tool, the description provides the essential elements: data source, param purpose, and return type. However, the returned DataFrame content is only described as '个股商誉减值明细' with no column details or examples, and there is no context on how to determine valid date values. Given the lack of an output schema and the presence of closely related sibling tools, the description leaves notable gaps.

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 has one parameter 'date' with a default but no description, and schema coverage is 0%. The description says '参考网站指定的数据日期' (refer to the data date specified on the website), which gives vague meaning: it is the report date. However, it does not specify the expected format (e.g., YYYYMMDD) beyond the default '20240630', nor does it explain how dates map to available periods. This is minimally helpful but insufficient for an agent to confidently construct the parameter.

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 returns '个股商誉减值明细' (individual stock goodwill impairment details) from EastMoney's data center, with a direct source URL. It is unambiguous about the resource being queried, though it lacks an explicit verb like 'get' or 'fetch'—the title and return statement imply retrieval. It does not distinguish this from closely related sibling tools such as stock_sy_hy_em or stock_sy_profile_em.

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?

No guidance is provided on when to use this tool versus alternatives like stock_sy_em, stock_sy_hy_em, or stock_sy_yq_em. There is no mention of prerequisites, scenarios, or when not to use it. The only hint is the source URL, but that does not help an agent choose among the many similar goodwill-related tools.

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