stock_zcfz_em

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

Annotations already establish that the tool is read-only, idempotent, and non-destructive. The description adds the return type (pandas.DataFrame) and the date range starting from 20100331, which is useful context. However, it does not disclose any additional behavioral traits such as potential errors, network dependencies, or handling of invalid dates, which would be helpful for a network-fetching tool.

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 a compact, well-structured docstring with clear sections for title, URL, parameters, return type, and return object type. Every element serves a purpose without redundancy, making it easy for an agent to parse and extract the essential information quickly.

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 tool is simple with one optional parameter and no output schema, and the description covers the source, parameter format, and return type. However, it lacks explicit usage rules (e.g., that 'date' is optional, how to handle unavailable dates, or when to choose this over sibling tools). These gaps reduce the overall completeness for an AI agent, though the core functionality is clear.

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 input schema only defines 'date' as a string with a default, but the description adds significant detail: it provides valid format examples ('20200331', '20200630', etc.) and states the start date ('从 20100331 开始'). This compensates for the schema's 0% description coverage and gives the agent concrete guidance on constructing valid input.

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 states the tool fetches a balance sheet from East Money's performance express section, as indicated by the title '东方财富-数据中心-年报季报-业绩快报-资产负债表' and the return type '资产负债表'. It clearly identifies the resource and the data category, but lacks an explicit verb like 'fetch' or 'retrieve' and does not differentiate itself from other balance sheet tools in the sibling list.

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 explicit guidance on when to use this tool versus alternatives. While it mentions the data source and the date format, it does not explain that this tool is specifically for performance express balance sheets or how it differs from other balance sheet tools like stock_balance_sheet_by_report_em. The implicit use case is inferred from the title and URL, but no exclusions or alternatives are given.

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