aggregate_logs

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

No annotations are provided, so the description carries the whole burden. It implies a read-only aggregation but doesn't state the window cap (43200 min), whether results are truncated, or anything about the shape of the returned counts; the 'recent window' phrasing is the only behavioral signal.

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 tight sentences with the operation front-loaded and an example that earns its place. No filler or 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?

All three params are documented and there's no output schema, so no return-value explanation is required. Still, for an aggregation tool with no annotations, the description doesn't clarify response shape (e.g. counts per group value) or the default window behavior, leaving a modest gap.

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 coverage is 100%, so enumerate/range details are already in the schema. The description reinforces the grouping ('by a dimension') and window concept but adds no syntax or default guidance beyond that, so baseline 3 applies.

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?

States a specific verb and resource (count log entries) plus the grouping and window scope, which tells an agent exactly what it returns. It doesn't name the overlapping metrics_* siblings (metrics_levels, metrics_routes), so differentiation is left to inference.

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?

'Call this to spot trends, e.g. error rate or status-code distribution' gives a positive usage cue. However, it never says when NOT to use it versus the many metrics_* and query_logs siblings that likely overlap, so an agent still has to guess.

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