Identify Recovering Dataset Trends

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, so the safety profile is covered. The description adds useful behavioral detail beyond the annotations by specifying the sort order and noting that the result includes pipeline-computed trend and publish-reliability evidence.

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 two concise sentences with no filler. The first sentence front-loads the core action and ordering; the second adds relevant information about included evidence without repeating schema or annotation content.

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?

With only one optional parameter and an output schema present, the description is largely complete: it states what is returned, the ordering, and the extra evidence included. A small gap is that it does not explicitly state the tool's scope or filtering behavior, but given the simplicity of the interface, this is minor.

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%, and the only parameter (limit) is fully documented in the schema with range and examples. The description adds no extra parameter semantics, but that is acceptable given the schema already carries the full weight for this simple parameter.

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 uses a specific verb ('Return') and a clear resource ('datasets whose published freshness trend is recovering'), and it states the ordering ('fastest staleness reductions first'). It is easily distinguished from sibling tools like find_stale or find_deteriorating because it targets recovery rather than current staleness or decline.

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 this tool would be useful—when the agent needs recovering freshness trends—but it does not explicitly say when to prefer it over alternatives or when not to use it. No sibling tools or exclusion conditions are mentioned, leaving the selection logic to inference.

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