macro_usa_cftc_merchant_goods_holding

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

Annotations already declare the tool as read-only, idempotent, open-world, and non-destructive, so the description does not need to restate safety. The description adds the data interval and source URL, which are useful contextual details beyond annotations. However, it does not disclose potential return volume, data granularity, or any limitations, leaving some behavioral ambiguity for a DataFrame-returning tool.

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 brief, consisting of a main sentence, a source URL, and standard Python docstring return lines. It front-loads the key identifying information. Some redundancy exists between the main sentence and the ':return:' line, both stating the report name, but overall the length is appropriate and free of unnecessary fluff.

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?

The description specifies the report type, date range, and return type (pandas.DataFrame), providing a foundational understanding. However, with no output schema, it does not describe the DataFrame's columns, data structure, or possible units, which an agent might need to interpret the result correctly. Given the tool's simplicity and zero parameters, the lack of output details is a moderate gap but not critical.

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 tool has zero parameters, and the schema is empty, so there are no parameter semantics to explain. The description does not add or contradict any parameter details. With no parameters, the baseline of 4 is appropriate because the schema trivially covers all parameters, and the description's mention of the date range is not parameter-related.

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 tool as the CFTC commodity commercial holdings report (商品类商业持仓报告) with a specified date range (19860115 to present). It clearly indicates a data retrieval function returning a pandas DataFrame, and the '商品类' qualifier helps distinguish it from currency or other holding types among sibling tools. However, it lacks an explicit verb like 'fetch' or 'query,' relying on the report noun and tool name to convey purpose.

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 alternatives like macro_usa_cftc_merchant_currency_holding or macro_usa_cftc_c_holding. It only states the data range and source URL, implying it covers historical CFTC commodity merchant holdings from 1986 onward, but does not explicitly state conditions or exclusions. This is a clear gap for an agent deciding among many similar CFTC/CME holding tools.

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