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

With no annotations, the description carries the burden of behavioral disclosure. It explains the event fields and, importantly, that the summary totals the entire filtered set. This is meaningful context for correct interpretation of results, though it omits minor details like default sort or pagination defaults (which live in the schema).

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?

Two dense, well-structured sentences with no filler. The first sentence front-loads the core purpose and dataset scope, and the second delivers a high-value usage tip about the summary field.

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 tool has no output schema and no annotations, the description compensates well by specifying event fields and summary fields. It could mention default sort/pagination behavior explicitly, but the schema already documents those, so the description is sufficiently complete for correct invocation.

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?

Schema description coverage is 100% with detailed parameter descriptions and enums, so the baseline is 3. The description adds little parameter-level meaning beyond schema; its main extra note about summary behavior is a result-interpretation hint rather than new parameter semantics.

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 resource: DeFi hack/exploit loss events plus a TradFi banking-loss reference set. It distinguishes this tool from the sibling tools (get_rates, get_usage, get_vault, etc.), which concern different data domains.

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 gives explicit practical guidance: for aggregated loss totals, read the summary instead of paging through events. It doesn't name alternative tools, but the siblings are not competing options for hack data, so exclusion guidance is unnecessary.

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