fund_lof_hist_min_em

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds context by specifying the return type (pandas DataFrame) and the data source URL, plus the parameter choices. However, it does not describe potential rate limits, output columns, or behavior with invalid dates/symbols, leaving some gaps beyond the 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 a compact docstring with a clear title line, a relevant URL, parameter definitions, and a return type. It is front-loaded with the core purpose and follows a consistent structure. No redundant fluff, though the return line '每日分时行情' partially repeats the title.

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 5-parameter data retrieval tool with no output schema, the description provides the essential purpose and parameter documentation. But it lacks example calls, clarification of the DataFrame structure, and details on date/time formatting or timezone handling. It is adequate for basic use but not fully complete for an agent to invoke correctly in edge cases.

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?

With schema description coverage at 0%, the description compensates by listing all 5 parameters with types and enum-like choices (period: 1,5,15,30,60; adjust: '', qfq, hfq). It gives basic descriptions like 'LOF 代码' and '开始日期时间'. However, it does not specify date format expectations, the meaning of 'qfq'/'hfq' (forward/backward adjustment), or clarify that period values are in minutes, leaving room for ambiguity.

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 states '东方财富-LOF 分时行情' (Eastmoney LOF minute-level market data), specifying the resource (LOF funds) and data frequency (intraday). The name and return type '每日分时行情' further clarify it provides historical minute-by-minute data. It distinguishes from sibling tools like fund_lof_spot_em (spot) and fund_lof_hist_em (likely daily) via the 'min' in the name and the period choices, though it does not explicitly call out differences.

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 implies its usage through the tool name and parameter choices (period options like '1', '5', '60' for minutes) and the return statement '每日分时行情', suggesting it is for intraday/minute-level historical LOF data. However, it provides no explicit guidance on when to use this tool versus alternatives, no exclusions, and no mention of scenarios where other tools would be more appropriate.

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