macro_china_society_electricity

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the source URL and return type (pandas.DataFrame), which is useful context, but it doesn't disclose data freshness, column structure, or any potential fetching issues. With annotations doing heavy lifting, this is adequate but not rich.

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 very short and front-loaded with the title, followed by the source URL and return type in standard structured format. It wastes few words, though the first line duplicates the annotation title, which is a minor redundancy.

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 no-parameter, read-only fetcher with strong annotations, the description is minimally viable: it specifies the data source and return type. However, there is no output schema, and the description does not elaborate on the actual columns, data granularity, or meaning of 'classification' beyond the title, which could be important for an AI agent deciding if this meets a user's request.

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 input schema is empty, so the description carries no parameter burden. Per the guideline, a baseline of 4 is appropriate when there are no params to explain.

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 returning a table of China's total society electricity consumption classification from Sina Finance, and the specific topic distinguishes it from sibling macro tools. However, the verb is implicit ('return' rather than an explicit action like 'fetch'), so it's clear but not maximally explicit.

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 information is provided about when to use this tool versus alternatives, such as related macro_china_energy_index or macro_china_daily_energy. There are no exclusions, prerequisites, or contextual hints about selecting this over other macro data tools.

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