stock_hsgt_fund_min_em

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is covered. The description adds the return type (pandas.DataFrame) and the symbol choices, but reveals no other behavioral traits such as data granularity, refresh frequency, or potential quirks. With annotations handling the baseline, 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 concise and well-structured with a title, source URL, parameter documentation, and return type. Each line serves a purpose, though the title duplicates the annotation title and the URL may be of limited use to an AI agent. Overall, it is efficient and not 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?

With a single parameter, rich annotations, and no output schema, the description is adequate but has gaps. It tells the agent what the data represents at a high level (沪深港通持股-分时数据) but does not specify DataFrame columns, time range, or the meaning of '分时' in practical terms. The absence of usage guidelines further reduces 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 input schema provides only a default value with no description or enum, and schema coverage is 0%. The description compensates by explicitly listing valid choices: '北向资金' and '南向资金', clarifying the acceptable values for the symbol parameter. This is valuable semantic information beyond the structured 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 identifies the specific resource (沪深港通市场概括分时数据) and scope (北向/南向资金), distinguishing it from sibling HSGT tools like stock_hsgt_hist_em and stock_hsgt_fund_flow_summary_em by focusing on intraday (分时) data. However, it lacks an explicit verb (e.g., '获取' or 'fetch'), relying on the tool name and context to infer the action.

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 versus alternative HSGT-related tools. It does not mention scenarios, exclusions, or alternatives, leaving the agent to infer usage from the data type alone. This is a clear gap given the many similar sibling tools.

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