get_receipt

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context: when start_date/end_date are empty, they default to today; when vars_list is empty, it returns all commodities. It also states the return type as pandas.DataFrame, which is helpful. It does not disclose data sources or update frequency, but given the annotations, the added defaults and return type earn a solid 4.

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 concise docstring with clear sections for parameters and return, and it is appropriately sized for three parameters. It includes redundant ':type' lines that restate the schema types, but these are brief and not excessive. The structure is front-loaded with the purpose line, making it easy to scan.

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 no output schema, the description correctly states the return type but does not explain the columns, data source, or the full scope of coverage (e.g., which exchanges or whether it aggregates all exchanges). Given the large sibling set of warehouse receipt tools, this lack of scope detail prevents an agent from confidently choosing this tool over exchange-specific ones. It is adequate but has clear gaps.

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 for parameter meaning. It explains date formats (YYYY-MM-DD, YYYYMMDD, datetime.date), default behavior when empty, and gives examples for vars_list ('RB', 'AL'). However, there is a type inconsistency: the description says vars_list is of type str, while the schema declares it as an array. This could confuse an agent, keeping the score from being 5.

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 states '大宗商品-注册仓单数据' (Commodities - Registered Warehouse Receipt Data) and the return type as pandas DataFrame, making it clear the tool retrieves warehouse receipt data. However, it does not explicitly differentiate itself from the many exchange-specific warehouse receipt siblings (e.g., futures_warehouse_receipt_czce), leaving its exact scope somewhat ambiguous. The verb 'get' in the tool name adds clarity, but the description is largely a noun phrase.

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 the numerous alternative warehouse receipt tools among the siblings. It does not mention any exclusions, prerequisites, or scenarios where another tool would be more appropriate. Blank default behavior is described (e.g., empty date means today), but this is parameter behavior, not usage context.

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