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

Annotations already declare readOnlyHint and destructiveHint false, so no contradiction exists. The description adds useful behavioral context beyond annotations by describing what each study returns, the data sources used, and the fact that no API key is required.

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 information-dense and front-loaded with the core concept. The enumerated shock types are specific and useful, and each sentence contributes meaningful context. It is slightly long but not wasteful.

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 read-only list/detail tool with no output schema, the description covers enough: what the tool is about, the events included, the fields returned, the data source, and auth expectations. The optional slug behavior is documented in the schema.

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 100% description coverage for the single optional slug parameter, which is already well explained. The tool description does not add further parameter-level semantics, so the baseline of 3 is appropriate.

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 states that the tool provides pre-registered event studies of major shocks to US containerized trade and lists specific examples such as tariff waves, COVID, and the Red Sea null. It also says each study returns registration, verdict, evidence, and falsification test, so the resource and deliverable are clear. However, it does not explicitly differentiate itself from sibling tools like tariff_story or trade_query.

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 implies when to use the tool: when an agent needs pre-registered causal event studies of trade shocks. It also provides helpful context about data sources and that no API key is required. But it does not explicitly say when to prefer this tool over alternatives or when not to use it.

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