stock_fhps_em

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

The annotations already establish that the tool is read-only, idempotent, and non-destructive. The description adds that the return type is pandas.DataFrame, which is useful, but it doesn't disclose any additional behavioral traits such as date format restrictions, pagination, or live website scraping. Minimal extra transparency 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 concise and follows a docstring structure with a title, source URL, and parameter/return documentation. Every line serves a purpose with no extraneous text, though the phrasing is terse and lacks a full sentence.

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 one-parameter read-only tool, the description covers the source, parameter meaning, and return type. However, it omits details about the DataFrame's columns, the scope (e.g., all A-shares), and any date range limitations. Without an output schema, more detail would be expected for full completeness.

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 provides only the parameter name and default with no description (0% coverage). The description compensates by explaining that 'date' refers to the 分红送配报告期 (dividend distribution report period), clarifying its meaning and format implied by the default. This adds meaningful semantic value.

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 retrieving dividend/distribution data from East Money's Data Center, as seen in the title and URL. It states a return type of pandas.DataFrame, implying a data retrieval action. However, it lacks an explicit verb and doesn't differentiate from similar sibling tools like stock_fhps_detail_em.

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?

There is no guidance on when to use this tool versus alternatives. The description only provides a parameter and return type, with no context about use cases, prerequisites, or alternative tools. This is a significant gap given the many dividend-related sibling tools.

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