stock_institute_hold_detail

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 useful context: it specifies the return type (pandas.DataFrame) and the data source (Sina Finance). It does not mention error behavior, rate limits, or data coverage limitations, so moderate transparency 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 well-structured: a title/URL line, then parameter and return documentation. The detailed quarter format explanation is essential and presented efficiently with examples. The URL adds some redundancy but does not significantly bloat the description.

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 covers parameter semantics fully, specifies the return type, and mentions the data source. Rich annotations handle safety aspects. Although there is no output schema, the return-type description is adequate for an agent to know what to expect. Specific DataFrame columns are not described, but this is not critical for tool selection and invocation.

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?

With schema description coverage at 0%, the description fully compensates. It thoroughly explains the 'quarter' parameter: the mapping from report types (一季报=1, 中报=2, 三季报=3, 年报=4), the starting year (2005), and concrete examples ('20191', '20193'). The 'stock' parameter is defined as '股票代码' (stock code), providing clear meaning beyond the bare schema.

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 institutional holding data for a specified stock and financial reporting quarter, with 'return' explicitly documented. The resource and scope are specific (Sina Finance institutional holdings detail), but it does not explicitly contrast with sibling tools like stock_institute_hold, relying on the 'detail' suffix in the name.

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 detailed formatting guidance for the 'quarter' parameter, including valid values and examples, which aids correct invocation. However, it offers no guidance on when to use this tool versus alternatives such as stock_institute_hold or other holding-related functions, and no exclusions or alternative suggestions are given.

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