Aggregate stats over a LOG store

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

The description adds context about grouping and metrics, but annotations already declare readOnlyHint=true and destructiveHint=false, so the description's behavioral insight is limited. It does not cover rate limits, authentication, or data volume effects, but the annotations mitigate the need. No contradictions.

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 concise sentences: the first delivers the core functionality, the second adds usage guidance. No fluff, every word earns its place.

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?

Despite the clear purpose, the description omits the return format (e.g., structure of aggregated results). Without an output schema, an agent needs to know what the response looks like to parse it. This is a significant gap for a grouping + metrics tool.

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 parameters are already described. The description adds value by explicitly listing the valid values for groupBy (hour/day/source/level) and metric (count/avg/max/min/sum), which are not fully captured in the schema (groupBy is just 'string' with no enum). This extra detail helps the agent choose correct values.

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 it computes aggregate statistics over LOG entries, lists valid group-by options and metrics, and distinguishes from pulling raw entries. It uses a specific verb (Compute) and resource (aggregate stats over LOG store), and effectively differentiates from siblings like rootr_query_log_entries.

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 explicitly notes that it is 'useful for trend/volume questions without pulling raw entries,' implying an alternative use case. However, it does not name the specific sibling tool (e.g., rootr_query_log_entries) for raw data retrieval, which would strengthen guidance.

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