car_market_cate_cpca

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

The annotations already declare this as a read-only, idempotent, non-destructive operation, so the safety profile is clear. The description adds the data source URL, parameter choices, and return type, but does not disclose any additional behavioral traits such as pagination, data freshness, or potential errors. It contributes only minimal value beyond the 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 a compact docstring with clear sections for URL, parameters, and return type. It is easy to scan and contains no redundant prose. However, it could be slightly more concise by removing the repetitive title line, but overall it is well-structured and appropriately sized.

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 the tool's simplicity (two parameters with provided choices) and strong annotations, the description is reasonably complete. However, it lacks information about the returned DataFrame's columns or any date/time aspects, and it does not mention how the URL is used. For a simple query tool this is adequate but leaves some ambiguity about the output structure.

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 provides only parameter names and defaults with zero description coverage. The description compensates by explicitly listing the allowed choices for both symbol ('轿车', 'MPV', 'SUV', '占比') and indicator ('批发', '零售'), which is essential for correct invocation. This is a meaningful addition over the bare schema.

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 data source (乘联会 CPCA) and the specific resource (车型大类 vehicle category), and the return type indicates it provides this statistical data. However, it lacks an explicit verb like 'get' or 'query' and does not differentiate this tool from sibling car market tools (e.g., car_market_segment_cpca), earning a 4 rather than a 5.

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?

There is no guidance on when to use this tool versus alternatives. The description does not mention that this tool is for vehicle category statistics while other sibling tools handle country, fuel, segment, etc. No exclusions or alternative suggestions are provided, so the agent must infer usage from the name alone.

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