macro_china_reserve_requirement_ratio

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is known. The description adds only the return type (pandas.DataFrame) and a source URL, but does not disclose any behavioral traits such as data granularity, network dependence, or potential delays. With no output schema, the description carries the burden of explaining what the agent can expect, and it falls short.

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 very short and does not waste words, but it mixes a Chinese term, a URL, and Python docstring conventions in a somewhat unstructured way. It is front-loaded with the indicator name, which is helpful. It earns a 4 for being brief, though it could be more coherent.

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?

This is a simple zero-parameter read-only tool, but the description lacks essential context about the data: what columns the DataFrame contains, whether it covers large/small financial institutions, historical depth, or update frequency. Since there is no output schema, the description should explain the return data's structure and scope, but does not.

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, so the baseline is 4. The schema coverage is trivially 100% with no properties, and the description does not need to elaborate on inputs. No additional parameter meaning is required.

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 names the resource (存款准备金率 - deposit reserve ratio) and states it returns a pandas DataFrame, but lacks an explicit verb (e.g., 'get', 'fetch') or scope (e.g., historical, current). It does not distinguish itself from other macro_china_* sibling tools beyond the name itself, making the purpose somewhat vague but still inferable.

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_china_money_supply or macro_china_pmi. There is no mention of prerequisites, typical use cases, or exclusions, leaving the agent without context for selecting this tool.

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