stock_margin_detail_szse

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds little beyond the return type (pandas.DataFrame) and the source URL. It does not disclose date format requirements, pagination, or any other behavioral traits, but with strong annotations this is acceptable.

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: a title line, a source URL, and structured param/return docstring entries. It avoids unnecessary prose and is easy to scan. The docstring formatting is standard and appropriate for the content.

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?

There is no output schema, so the description should explain the return data; it only says '融资融券明细' (margin trading details) as a DataFrame. It lacks column names, data granularity (daily vs. historical), or any explanation of what fields are included. For a financial data tool, this is insufficient for an agent to confidently use the result.

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 description coverage is 0%, so the description must compensate. It documents the 'date' parameter as '交易日期' (trading date), which adds meaning over the schema's raw string type. However, it does not specify the expected format (e.g., YYYYMMDD) beyond the default value, and no additional constraints or examples are given.

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's first line names the resource and scope: Shenzhen Stock Exchange margin trading transaction details. Although it lacks an explicit verb like 'retrieve' or 'list', the 'return' field (DataFrame) makes it clear this is a data retrieval tool. The exchange and '明细' (details) distinction help set it apart from summary or other-exchange margin tools, though it relies heavily on the tool name for full disambiguation.

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 no guidance on when to use this tool versus alternatives such as stock_margin_detail_sse or stock_margin_szse. There is no mention of use cases, exclusions, or prerequisites. The URL is a source reference but not usage guidance.

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