stock_hot_rank_detail_em

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, so the safety profile is clear. The description adds that the return type is pandas.DataFrame and that it contains historical trends and fan characteristics, which is useful but minimal. There is no detail on data granularity, columns, or potential pagination, so it only partially exceeds 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 extremely concise: a one-line title, a reference URL, and a compact docstring with parameter and return type. Every sentence is informative without unnecessary verbosity, and the structure follows a clear pattern (title, source, param, return).

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 single-parameter tool with no output schema, the description provides enough context: it names the source (Eastmoney), the data content (historical trends and fan characteristics), the required parameter format, and the return type. It lacks specifics about the DataFrame structure or date range, but given the simplicity of the tool, this is acceptable.

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. The docstring explains 'symbol' as a security code with market representation ('带市场表示的证券代码') and provides an example default 'SZ000665'. This adds meaning beyond the bare schema type and default, clarifying the expected format (exchange prefix + code).

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 historical trends and fan characteristics for a stock's popularity ranking on Eastmoney. The verb is implicit but the resource is specific ('个股人气榜-历史趋势及粉丝特征'), distinguishing it from real-time rank tools like stock_hot_rank_detail_realtime_em. However, it does not explicitly mention alternative sibling tools, so it loses one point for full differentiation.

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 implies usage for historical analysis ('历史趋势') but provides no explicit guidance on when to choose this over real-time rank tools or other popularity tools. There are no stated exclusions or alternatives, so the context is implied rather than explicitly directed.

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