index_kq_fz

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

The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the source URL and return type (pandas.DataFrame), which gives modest behavioral context beyond the annotations. It does not contradict the annotations and provides no negative side effects, but it does not deeply describe what the DataFrame contains.

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 structured as a compact docstring with a title, source URL, parameter definition, and return type. It is mostly efficient, though the return line repeats '中国柯桥纺织指数' after the initial title, which is mildly redundant. Overall, the format is organized and concise.

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 simple one-parameter tool with strong annotations, the description covers the essentials: what to pass and the return type. However, it does not describe the structure of the returned DataFrame (e.g., columns, time range, history) nor any query limitations. Given no output schema, this leaves some gap, but for such a straightforward index retrieval tool it is reasonably complete.

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 input schema has a single symbol parameter with 0% description coverage, so the description's explicit enumeration of valid choices ({'价格指数', '景气指数', '外贸指数'}) is essential and adds significant meaning. It tells the agent exactly what values are accepted, compensating for the schema's lack of detail. However, it does not explain the nuances of each choice beyond their literal names.

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 identifies the resource as '中国柯桥纺织指数' and implies retrieval of this index through its parameter and return documentation. It goes beyond a tautology by specifying the index source URL and that it returns a pandas DataFrame, making the tool's function clear even though no explicit verb like 'get' or 'fetch' is used.

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. It lists parameters and return type but does not explain context, exclusions, or relationships to sibling tools such as index_kq_fashion. The only implicit hint is that it serves data for the Keqiao textile index, but there is no explicit 'use this when' language.

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