Get Statistics of Recommendations Across Categories and Risks

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, so the description's 'Show statistics' is consistent. The description adds no additional behavioral details beyond the annotation set, meeting the baseline for transparency.

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 extremely concise with a single sentence purpose followed by three illustrative call examples. Every sentence adds value, and the structure is front-loaded for quick comprehension.

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 an output schema, return values are covered externally. The description explains the tool's purpose, parameters, and typical usage patterns (all vs. filtered). It is fully complete for its complexity level and sibling context.

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%, and the schema descriptions already explain parameters. The call examples add practical usage context (e.g., 'Statistics for the workspace workspace1') which enhances understanding beyond the schema, justifying a score above baseline.

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 explicitly states the tool shows statistics of recommendations across categories and risks, with a clear verb-resource combination. It distinguishes from sibling tools like advisor__get_rule_details or advisor__get_active_rules which focus on rules rather than aggregated statistics.

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 examples demonstrate standard usage and filtering by groups or tags, giving clear context for parameter use. However, it does not explicitly state when not to use this tool or provide alternative suggestions, leaving some ambiguity.

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