macro_china_daily_energy

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

The annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, covering the key safety aspects. The description adds the date range, source URL, and return type, which is useful context, but it does not disclose potential quirks like column names, units, or any processing limitations.

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, containing only the essential info: data subject, date range, source URL, and return type. It is somewhat unstructured as a single string, but it avoids unnecessary detail and is appropriately sized for a parameterless tool.

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 annotations and empty schema, the description provides the core details: what data, from when, from where, and the return type. However, it does not specify the columns or structure of the DataFrame, which would be helpful for a data tool. Without an output schema, this gap is noticeable but not critical for basic usage.

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 zero parameters, so the schema fully covers parameter semantics. The description adds context about the data being returned but does not need to explain parameters that don't exist. Baseline for 0 parameters is 4.

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 providing Chinese daily coastal six major power plant coal inventory data with a specific date range and source URL. Although it lacks an explicit action verb like 'get' or 'retrieve', the ':return: pandas.DataFrame' indicates the output type, making the purpose specific and distinguishable from sibling 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?

No guidance is given on when to use this tool versus alternatives such as macro_china_energy_index or other macro data tools. The description only states what data is returned, not under what circumstances it should be selected.

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