stock_hk_valuation_baidu

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 agent knows this is a safe, non-destructive read operation. The description adds that it returns a pandas.DataFrame and includes a source URL, but it does not disclose any further behavioral traits such as data freshness, rate limits, or authentication needs. This is adequate but not rich, given the safety annotations already cover the main concerns.

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 structured docstring with a title, URL, parameter definitions, return type, and return description. It is dense but not bloated, with no wasted words. The format is conventional and easy to parse, and the sample URL is useful. It could be slightly more concise by combining the title and URL, but it remains efficient.

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?

Given the tool's simplicity (3 optional parameters, no output schema, safe read annotations), the description is reasonably complete. It documents all parameter choices, identifies the data source, and specifies the return type. It does not explain the exact structure of the returned DataFrame or handle edge cases, but for a straightforward valuation data fetch, 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?

The schema has 0% description coverage, so the description carries the full burden for parameter documentation. It provides type and valid choices for indicator (总市值, 市盈率(TTM), etc.) and period (近一年, 近三年, 全部), and includes defaults for symbol and period. The sample symbol '06969' clarifies the expected HK stock code format. This goes well beyond the bare schema, though it does not explain the meaning of each indicator.

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 as obtaining valuation data for Hong Kong stocks from Baidu's stock platform, with a sample URL (https://gushitong.baidu.com/stock/hk-06969). The title '百度股市通-港股-财务报表-估值数据' and the name make the purpose unambiguous, and the Baidu source distinguishes it from Eastmoney-based sibling tools. However, there is no explicit verb like 'retrieve' or 'query', so it falls short of a fully specific verb+resource phrasing.

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?

No guidance is provided about when to use this tool versus alternatives. The description simply states the data source and parameters without mentioning any use cases, prerequisites, or exclusions. It neither names alternative tools nor gives context for when this Baidu-sourced data would be preferred over other HK stock valuation tools.

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