car_market_fuel_cpca

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the return type (pandas.DataFrame) and the source URL but does not describe the output structure, whether the symbol changes columns/rows, or any rate limits. This is adequate but not rich, so a 3 is appropriate.

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 extremely concise and follows a clear docstring structure: title, parameter with choices and type, source URL, and return type. No filler words or redundant content. Every line adds value, making it easy to parse quickly.

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, a description that lists the parameter values, provides the return type, and cites the source URL is largely complete. The only minor gap is not describing the data format or how the parameter affects rows/columns, but given the simplicity and no output schema, this is not a critical omission.

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%, leaving the description to compensate. The docstring explicitly lists the valid choices for 'symbol' (整体市场, 销量占比-PHEV-BEV, 销量占比-ICE-NEV), which are not present as enums in the schema. This is essential for correct use. It does not fully explain the meaning or output for each choice, but the labels are descriptive enough, so a 4 is warranted.

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 tool as retrieving CPCA new energy vehicle market segment data. The title '新能源细分市场' and the param choices (整体市场, 销量占比-PHEV-BEV, 销量占比-ICE-NEV) specify the resource and the type of data returned. While there is no explicit contrast with sibling tools, the unique '新能源细分市场' and 'fuel' context distinguish it enough from other car_market_* tools.

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 the symbol parameter choices and the source URL but does not explain when to choose this over car_market_total_cpca or other car_market cousins, nor does it state any exclusions or prerequisites. Usage must be inferred entirely from the title and parameter hints.

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