stock_us_hist

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

With annotations already declaring readOnlyHint=true, destructiveHint=false, and idempotentHint=true, the description adds modest extra context: the data source (Eastmoney URL) and the return type (pandas.DataFrame). It does not disclose potential caveats like rate limits, pagination, or the exact output columns, but the safety profile is well covered by 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 well-structured docstring with a title, URL, parameter list, and return type. Each line serves a purpose and is not redundant, aside from the first line duplicating the annotation title. It is appropriately sized for the tool's complexity.

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 5 parameters and no output schema, the description explains parameters and return type adequately. However, it does not enumerate the columns of the returned DataFrame (e.g., open, high, low, close, volume), which is significant given there is no output schema. It also omits date format details, leaving some ambiguity for an agent.

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 description coverage is 0%, so the description carries the full burden, and it does well: each parameter has a clear explanation, including the valid choices for `period` and `adjust`, and the important note that `symbol` must come from `stock_us_spot_em()`. The main gap is that date formats are not explicitly stated, though the schema defaults imply YYYYMMDD.

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 indicates this tool fetches daily market quotes for US stocks from Eastmoney, with a source URL provided. It does not explicitly differentiate from sibling tools like stock_us_daily or stock_us_hist_min_em, but the function name and parameters (start_date, end_date, period) make the historical daily data purpose clear.

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 a critical prerequisite: the `symbol` must be obtained from `ak.stock_us_spot_em()`'s `代码` field. However, it does not explicitly state when to prefer this tool over alternatives (e.g., minute-level history or spot quotes) or when not to use it. Usage is implied through the parameter choices and source, but no exclusions or comparisons are given.

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