stock_jgdy_detail_em

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, so the safety profile is covered. The description adds minimal behavioral context: it's a scraper for a specific Eastmoney page, returns a pandas DataFrame, and requires a start date. It doesn't describe error behavior, rate limits, or pagination, but with annotations covering safety, this is acceptable though 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 concise and structured with a clear title, URL, param doc, and return type. It uses a consistent docstring format. The lines are short and informative, though the URL line could be considered redundant and the name itself is very long. Overall, every part earns its place, but the format is a bit terse.

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 medium complexity (1 param, no output schema, no nested objects), the description is incomplete. It doesn't specify the exact structure of the returned DataFrame, what columns to expect, or whether the date parameter is inclusive/exclusive. The URL adds context but doesn't substitute for missing return schema details. With siblings like stock_jgdy_tj_em, more behavioral hints would help disambiguate.

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 coverage is 0%, meaning the description carries the burden of explaining the 'date' parameter. It says 'date: 开始时间' (start time) and provides a default example '20241211', roughly implying a YYYYMMDD format. This is minimal but some meaning is conveyed. However, it's not clear if this is a start date for a range or a specific reporting date, and no details on format variations are given.

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 says it retrieves institutional research details (机构调研详细) from a specific Eastmoney data center page. The verb 'detail' is specific, but it could be confused with sibling tool stock_jgdy_tj_em (机构调研统计) which is about the same domain. It distinguishes by saying '详细' (detail) rather than '统计' (statistics), but doesn't explicitly name alternatives.

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 gives no explicit guidance on when to use this tool versus alternatives. It mentions the URL and a single parameter (date), but does not state prerequisites, limitations, or when a user would prefer this over the statistical sibling tool. The context implies it's for historical institutional research detail data, but no clear usage scenario is provided.

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