energy_carbon_sz

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

Annotations already declare the tool as safe, read-only, idempotent, and non-destructive. The description adds the source URL and daily frequency (每日行情数据), providing some behavioral context beyond annotations. However, it does not disclose data freshness, time range, or caveats about the underlying source, so the added value is minimal but not absent.

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 compact and uses a conventional docstring format with :return: and :rtype:. The URL is useful. However, the title is duplicated from the annotations, which adds minimal redundancy. Overall, it is appropriately sized and front-loaded.

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?

There is no output schema, so the description must explain the return value. It only says '国内碳情每日行情数据' (domestic carbon daily market data), which is vague. It does not specify what fields or columns the DataFrame contains, nor whether it covers a specific date or a historical range. For a tool with no parameters, the default behavior should be more explicitly documented.

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 there is nothing to document. The empty schema fully defines the interface, and the description does not need to add parameter details. The baseline of 4 is appropriate given the lack of parameters.

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 (Shenzhen Carbon Emissions Exchange) and the data type (domestic carbon daily market data), distinguishing it from regional siblings like energy_carbon_bj and energy_carbon_gz. The return line reinforces that it provides daily quotes as a DataFrame, making the purpose unmistakable despite the lack of an explicit verb.

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?

No guidance is provided on when to use this tool versus alternatives such as energy_carbon_bj, energy_carbon_gz, or energy_carbon_domestic. The only differentiation is the exchange name in the title, but there is no explicit when-to-use or alternative naming, leaving the agent to infer selection based on the tool name alone.

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