stock_gsrl_gsdt_em

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

Annotations already declare the tool as read-only, idempotent, and non-destructive, so the description does not repeat these. It adds that the date parameter is a trading day and that the result is a pandas DataFrame, providing some behavioral context. However, it does not disclose details like data granularity, potential network behavior, or exact contents, so the added value is moderate.

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 compact, consisting of a title line, a URL, and a concise docstring for the parameter and return. It is front-loaded with the source name and presents information in a structured, scannable format with no redundant wording.

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 parameter and no output schema, the description covers the source, parameter, and return type, which is adequate for basic use. However, it does not explain what columns or event types are included in the returned 'company dynamics' DataFrame, nor does it explicitly state the date format. An agent would need additional inference to fully understand the output content.

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, type, and default value, with no descriptions. The description compensates by stating ':param date: 交易日' (trading day) and using the default '20230808' to imply a YYYYMMDD format. This clarifies the meaning and format of the parameter, though it could be more explicit about allowed date ranges or edge cases.

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 essentially repeats the title '东方财富网-数据中心-股市日历-公司动态' and adds a URL and docstring. It clearly implies the domain (company dynamics in the stock calendar) but lacks an explicit action verb like 'retrieves' or 'queries'. The return type as a pandas DataFrame and the date parameter strongly hint at data retrieval, making it somewhat clear, but the purpose is not stated as a directive.

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 about when to use this tool versus alternative tools for similar data. The description does not mention prerequisites, exclusions, or alternative tools. It only provides the parameter and return specification, leaving the agent to infer when this tool is appropriate.

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