stock_yjkb_em

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds some useful context: it gives the data source URL, specifies that the date parameter starts from 20100331, and indicates the return type (pandas.DataFrame). However, it does not disclose any additional behavioral traits such as network request behavior, pagination, data freshness, or potential delays, which would be valuable but are not strictly required given the strong annotation coverage.

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 reasonably concise and well-structured, using a docstring-like format with a title line, URL, and parameter/return documentation. The URL provides a useful reference for the data source. There is slight redundancy with the title being repeated in the description, and the return line is minimal, but overall every line contributes functional information without excessive padding.

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 tool with a single parameter and no output schema, the description covers the essential usage details: data source, parameter format/range, and return type. It is adequate for basic retrieval. However, it lacks any discussion of the DataFrame's columns or contents, and it does not qualify how this tool relates to similar financial reporting tools, which is contextually relevant given the large sibling set. An agent might successfully invoke it but may not understand what data fields to expect.

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 provides only the property name 'date', type string, and a default value, with 0% schema description coverage. The description compensates well by explaining the parameter as '财报发布日期' (earnings report release date), providing concrete format examples ({'20200331', '20200630', ...}), stating the valid range (from 20100331), and confirming the type as str. It also documents the return type as pandas.DataFrame. This adds substantial 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 identifies the tool's purpose as accessing East Money's performance express (业绩快报) data by date, as seen in the title and URL. It specifies the resource (annual/quarterly report express data) and the key parameter (report release date). However, it does not explicitly distinguish this tool from closely related siblings like stock_yjbb_em (业绩报表) or stock_yjyg_em (业绩预告), which could lead to ambiguity.

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. It only documents the 'date' parameter and return type. Given the presence of many sibling tools for financial data (e.g., stock_yjbb_em, stock_yjyg_em), it would be valuable to state that this tool is specifically for 业绩快报 (performance express) data, but no such comparison or usage context is given. This is essentially no guidance.

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