energy_carbon_bj

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

The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, which cover safety expectations. The description adds the source URL and the return type (pandas.DataFrame), which is useful but minimal. It does not disclose additional behavioral traits like data freshness, pagination, or error handling. Given the annotations cover the key safety profile, this meets the baseline but does not exceed it.

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 concise and front-loaded with the Chinese title, followed by the source URL and return type. Every line adds value: the title states the purpose, the URL provides traceability, and the return type informs output handling. It is somewhat terse and could be better structured with a separate description sentence, but it avoids waste and is appropriately short.

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?

With no output schema and no parameters, the description carries the burden of explaining what data the function returns. It states 'public trading market data' and specifies the source, but it does not detail the columns or structure of the DataFrame (e.g., price, volume, date fields). For a simple market data tool, this is minimal but acceptable; additional details would improve completeness.

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, and the schema reflects that with an empty properties object. With no parameters to document, the description has no burden to explain semantics. The baseline is 4 for zero-parameter tools, and the description correctly adds no irrelevant param information.

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 explicitly states that the tool provides public trading market data for Beijing's carbon emissions trading platform. It names the specific resource (Beijing carbon emissions) and distinguishes it from sibling tools like energy_carbon_gz or energy_carbon_sz by the 'bj' suffix and the explicit '北京市' reference. Though no explicit verb like 'get' is used, the noun phrase '公开交易行情' clearly indicates a data retrieval function.

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 clearly identifies the tool as specific to Beijing's carbon market, implying it should be used when Beijing carbon trading data is needed. However, it does not explicitly mention alternatives or state when not to use it, such as pointing to other regional carbon tools (e.g., energy_carbon_sz for Shenzhen). With many similar sibling tools, explicit guidance would be valuable, but the Beijing-specific wording gives clear context.

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