stock_qsjy_em

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

Annotations already indicate a safe, idempotent read operation. The description adds the data range starting from 2010-06-01 and the parameter format example, which provides useful context beyond the annotations. It does not contradict the annotations and discloses the return type as pandas DataFrame.

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 concise and follows a standard docstring structure with source URL, parameter documentation, and return type. It is not overly verbose, and each line provides relevant 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?

Given the tool's simplicity (one parameter, no output schema) and the presence of helpful annotations, the description provides adequate context: data source, parameter semantics, and return type. It does not describe the DataFrame columns, but this is not critical for such a simple data lookup.

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 description explains that the 'date' parameter is the data month and gives an example (2011-07-01), adding meaning beyond the bare schema which only lists a default value. However, the default in the schema is '20200731' without dashes, conflicting with the example format and leaving ambiguity about the accepted date format.

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 data resource (券商业绩月报 from Eastmoney) and the URL, and the return type is specified as a pandas DataFrame. However, it lacks an explicit verb like 'retrieve' or 'get', relying on implied meaning from the docstring structure. It distinguishes from siblings by naming the specific dataset.

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 gives the data source and parameter guidance, implying that this tool is for retrieving monthly brokerage performance data from Eastmoney. It does not explicitly state when to use this tool versus other stock data tools, nor does it mention alternatives or exclusions.

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