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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description does not need to restate safety. It adds useful output context by listing time-correctness rules and agent_hints, but does not describe behaviors like error handling or response shape; the output schema covers that.

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 one dense, scannable sentence with the main point front-loaded ('FULL semantics of one dataset') and key items listed compactly. The time-correctness warning is flagged with a star and 'read before backtesting', adding priority without bloat.

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 rich output schema and single required parameter, the description sufficiently explains what content is returned and when to use it. Nothing critical is missing for an agent deciding between this and similar data tools.

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?

There is one parameter, dataset_id, and the description confirms it via 'Args: dataset_id' while 'one dataset' implies an identifier context. However, schema description coverage is 0%, and the description does not explain how to obtain or format a dataset_id, such as from list_datasets.

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 states exactly what the tool does: it returns FULL semantics for one dataset, enumerating grain, fields, time-correctness rules, relations, agent_hints, and quant_use. This content set clearly distinguishes it from siblings like query_dataset or list_datasets.

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?

It gives an explicit trigger: read time-correctness rules before backtesting. It does not formally name alternatives or when-not to use it, but the 'before backtesting' context and semantic focus make the intended usage clear.

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