get_roll_yield_bar

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds useful behavioral detail beyond this: it specifies the return type (pandas.DataFrame), the 'ry' column, and whether the index is date or variety, and reveals that 'symbol' mode returns closing prices. No contradictions with 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 organized as a docstring with :param and :return labels, front-loading the core type_method behavior. Each line is informative and there is no fluff; while dense, the length is justified by the need to document five parameters at zero schema coverage.

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?

Given that there is no output schema and the schema lacks descriptions, the description provides a compact output definition and mode explanations, making the tool usable. It could be improved by explicitly mapping which parameters are relevant per type_method and mentioning defaults (visible in schema), but the core information is present.

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 of parameter semantics. It documents type_method with three enumerated behaviors, gives var examples ('RB', 'AL'), and provides date formats (YYYYMMDD) for date, start_day, and end_day. It lacks explicit guidance on how start_day/end_day combine with date/type_method, but overall it compensates well for the missing schema descriptions.

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 the resource (展期收益率/roll yield data) and provides three explicit query modes via type_method, each with a specific output (closing prices, cross-sectional roll yields, time series). However, it does not distinguish this tool from the similarly named sibling get_roll_yield, so purpose is clear but sibling differentiation is absent.

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 gives concrete scenarios for each type_method value ('symbol' for all contracts of a variety on a date, 'var' for cross-section across varieties, 'date' for time series per variety), which tells an agent when to choose each mode. It does not explicitly name alternatives or exclusions relative to sibling tools, so it falls short of a full when/when-not statement.

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