stock_concept_fund_flow_hist

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

The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds that it returns a pandas.DataFrame and specifies the parameter as a concept name, but does not disclose additional behaviors like data range, frequency, or limitations. With annotations covering the safety aspect, this is acceptable but not enriched.

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 docstring-style block with title, URL, and param/return annotations. It is not front-loaded with a clear single sentence, and the URL is arguably unnecessary for an agent. However, it is not overly long and each line carries some information, so it earns a middle score.

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?

There is no output schema, so the description should explain the return value. It states the return type is 'pandas.DataFrame' and describes it as '概念历史资金流' (concept historical capital flow), but does not detail the columns or the historical range. Given the tool's simplicity, this may be sufficient for an agent to infer the output, but it lacks explicit column information that would help with consuming the result.

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 has one parameter 'symbol' with zero description coverage. The description compensates by stating ':param symbol: 概念名称' (concept name), which is exactly what the parameter expects. It also provides a default value ('数据要素') in the schema, giving concrete context. For a single-parameter tool, the description adequately explains the parameter's meaning.

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 resource: '东方财富网-数据中心-资金流向-概念资金流-概念历史资金流' (Eastmoney concept historical capital flow), and the return type 'pandas.DataFrame' indicates it fetches data. The tool name also confirms the purpose. However, it lacks an explicit verb like 'get' or 'retrieve', and the title is more of a navigation path than a sentence.

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 on when to use this tool versus alternatives. The description does not mention any conditions, exclusions, or recommend other tools (e.g., stock_sector_fund_flow_hist for industry sectors). Usage is only implied by the name and title.

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