stock_margin_underlying_info_szse

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

Annotations declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is already covered. The description adds that it returns a pandas.DataFrame and includes the source URL, but it does not describe potential caveats like date validity or result contents beyond '标的证券信息'. This is adequate but not rich, so a 3 is 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 and well-structured: a title line, a source URL, and standard param/return docstring lines. No fluff or redundancy. It's appropriately sized for a one-parameter read-only tool, though it could be slightly more informative about output columns.

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 tool with good annotations and a single parameter, this is mostly complete: it states the data source, the parameter meaning, and the return type. However, it does not describe the actual columns or contents of the returned DataFrame, and there is no output schema to compensate. This leaves the agent somewhat in the dark about the exact data structure, so a 3 is warranted.

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 0%, so the description must compensate. It defines the 'date' parameter as a '交易日' (trading day), which adds business meaning beyond the schema's plain string type. The default value '20221129' in the schema also suggests format, but the description itself doesn't explicitly state the YYYYMMDD format. Still, it meaningfully clarifies the parameter's semantics.

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 identifies the resource as '深圳证券交易所-融资融券数据-标的证券信息' (Shenzhen Stock Exchange - Margin Trading Data - Underlying Securities Information) and includes the source URL. It distinguishes from siblings like stock_margin_underlying_info_bse by specifying the exchange in both the name and description. However, it lacks an explicit verb like 'get' or 'retrieve', which prevents a perfect score.

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 provides clear context: it is for SZSE margin trading underlying securities data, with a date parameter. The URL and title establish the intended use case without explicitly stating when not to use it or naming alternatives, which fits the 'clear context, no exclusions' level. It doesn't explicitly contrast with BSE/SSE counterparts, but the exchange is clearly identified.

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