stock_profit_forecast_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 covered. The description adds useful context: the data source (Eastmoney), the default behavior of returning all data, and the ability to filter by industry sector. It does not mention pagination, update frequency, or rate limits, but the simple read-only nature keeps this acceptable.

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 combines source, URL, parameter documentation, and return type in a small docstring. There is no filler or redundant text. It could be slightly more front-loaded with an explicit action statement, but it remains efficient and scannable.

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 one optional parameter, no output schema, and strong annotations, the description covers the essential aspects: source, filter semantics, and return type as a pandas DataFrame. It does not list output columns or clarify how it differs from similar profit-forecast tools, but those are secondary for this simple read-only endpoint.

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?

Schema description coverage is 0%, so the description carries the full burden for the single 'symbol' parameter. It fully explains that an empty string ('') returns all data, a specific sector name like '船舶制造' filters by industry board, and that valid board names can be retrieved via ak.stock_board_industry_name_em(). This is comprehensive and actionable.

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 Eastmoney's profit forecast (盈利预测) data from the research report center and lists the source URL. It implies retrieval via the docstring's ':return: 盈利预测' and 'pandas.DataFrame', but lacks an explicit verb such as '获取/查询'. The source name '东方财富网' helps distinguish it from similar sibling tools like stock_profit_forecast_ths, though it does not explicitly contrast them.

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 such as stock_profit_forecast_ths or stock_research_report_em. It only explains the symbol parameter behavior (empty means all data, a sector name filters by industry), which is parameter usage rather than tool-selection guidance.

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