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. The description adds call examples but no additional behavioral context such as permission requirements, response size, or error states. It is adequate but not enhanced beyond the annotations.

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 very concise: one sentence stating purpose followed by three short call examples. Every part is useful and front-loaded, with no redundant information.

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?

For a tool with two optional parameters, an output schema, and no nested objects, the description sufficiently covers purpose and usage patterns. The examples demonstrate realistic invocation, making it complete for an AI agent to use effectively.

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% with clear descriptions for 'groups' and 'tags'. The description provides usage examples but does not add semantic meaning beyond the schema, so it meets the baseline without further contribution.

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 the verb 'Show statistics of recommendations across categories and risks.' It identifies a specific resource and scope, distinguishing it from sibling tools like advisor__get_active_rules which focus on individual rules rather than aggregate 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?

The description provides concrete examples of calls (standard, filtered by groups, filtered by tags), clearly illustrating usage contexts. However, it does not explicitly mention when not to use this tool or suggest alternatives among siblings, which would improve guidance.

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