stock_hold_num_cninfo

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, covering the safety profile. The description adds context about the accepted date range (from 20170331) and the date format pattern (quarter-end dates), which is useful. However, it does not disclose what columns the returned DataFrame contains, whether any parameters are required, or any potential edge cases (e.g., empty results for unsupported dates). Given the annotation coverage, this is adequate but not rich.

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 docstring structure with param, type, return, and rtype sections. It includes a URL for source reference, which is useful but not strictly necessary. The use of Chinese title and docstring is efficient and not overly verbose.

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, read-only tool with good annotations, the description covers the core usage. However, it leaves ambiguity about the scope of the returned data (whether it applies to all A-shares or a specific stock) and does not describe the DataFrame columns. No output schema exists, so the description should carry a bit more burden here. It is minimally viable but has gaps.

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 defines 'date' as a string with a default, providing no semantic meaning. The description compensates fully by specifying the exact format (XXXX0331, XXXX0630, XXXX0930, XXXX1231), the start date, and the type. This is clear, actionable guidance that goes well beyond the 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 defines the tool's scope: retrieving shareholder count and holding concentration (股东人数及持股集中度) from CNINFO's thematic statistics. It specifies the data source via URL and the return type as a pandas DataFrame. It is not a tautology, but the Chinese title serves as the primary description and lacks an explicit English verb like 'retrieve' or 'query', which prevents a perfect score.

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 compared to similar sibling tools such as stock_zh_a_gdhs or stock_hold_change_cninfo. It does not mention alternatives, exclusions, or specific scenarios. The only usage-related context is the date format and availability start date, which is parameter guidance 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.