stock_xjll_em

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

Annotations already establish the tool as read-only, idempotent, and open-world, so the description does not need to repeat that. It adds useful context: the source URL, valid date format (YYYYMMDD), and the starting date 20100331. However, it does not disclose potential failure modes, network dependency, or response behavior beyond returning a DataFrame, leaving some behavioral uncertainty.

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, front-loaded with a title and URL, then structured as a docstring with param and return sections. It avoids redundancy except the repetition of '现金流量表' in title and return, which is minor. It is easy to scan and contains no filler.

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 1-parameter read-only tool with no output schema, the description sufficiently covers the input contract and return type. It identifies the data source, the date constraint, and the output as a pandas DataFrame. It lacks details about the DataFrame's columns and usage context, but given the low complexity and rich annotations, it is adequate for an agent to invoke correctly.

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 provides only a string type and default with no description, so the description carries full burden for the single parameter. It explains the date format, gives example values (20200331, 20200630), and specifies that valid dates start from 20100331, which is essential for correct invocation. It does not explicitly state that dates must be quarter-end, but the examples strongly imply it, so the semantics are adequately covered.

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 identifies the resource as a cash flow statement (现金流量表) from East Money's data center, with a source URL. It clearly states the domain (年报季报/业绩快报) and return type, distinguishing it from other statement tools like stock_zcfz_em (balance sheet) and stock_lrb_em (income statement). However, it lacks an explicit verb like 'get' or 'retrieve' and could be mistaken for other cash-flow tools (e.g., stock_cash_flow_sheet_by_report_em) without additional context.

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. The description does not indicate whether to choose this over stock_cash_flow_sheet_by_report_em or other statement tools, nor does it mention prerequisites or exclusions. It only describes the tool's own behavior.

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