stock_yjyg_em

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safe read profile is covered. The description adds context about the data source (East Money), URL structure, and date availability ('从 20081231 开始'), which is useful. However, it does not disclose output columns, pagination, or other behavioral details, so it only partially adds value 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 follows a clear docstring structure with title, URL, parameter, and return sections. It is front-loaded with the purpose. There is minor redundancy since the title repeats the annotation title, but overall every line serves a purpose.

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 single-parameter read-only tool, the description covers the basics: source, parameter, and return type. However, the return is described only as '业绩预告' with no detail about DataFrame columns or content, which is insufficient since there is no output schema. The tool's context is not fully specified for an agent to judge if it meets the user's needs.

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 provides a default value with no description (0% coverage). The description compensates by documenting the 'date' parameter as the financial report release date, listing valid examples (20200331, 20200630, etc.), and noting the earliest available date (20081231). This adds meaningful semantics beyond the schema, though it could be more explicit about the exact format and whether only quarter-end dates are accepted.

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 retrieves annual/quarterly performance forecasts (业绩预告) from East Money's data center, as shown in the title and the ':return: 业绩预告' line. The URL provides a concrete reference. However, it does not explicitly distinguish itself from sibling tools like stock_profit_forecast_em, which also deals with profit forecasts.

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 lacks any mention of exclusions, alternative tools, or specific use cases. The only guidance is parameter-related, which is not usage context.

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