get_recommendation_quality

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It describes the tool's output and intent but does not disclose any behavioral traits such as whether it is read-only, requires authentication, or has rate limits. The term 'flywheel' is metaphorical and does not clarify behavior.

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 paragraphs: the first explains the concept, the second lists use cases. It is front-loaded and every sentence adds value. It could be slightly more concise, but it is well-structured and readable.

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 no output schema, the description adequately explains what the tool returns: metrics on recommendation action rates and savings accuracy, plus 'verified-savings proof'. It does not specify the exact structure (e.g., per recommendation type list), but the description is sufficient for understanding the tool's purpose and output.

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?

The input schema has no parameters, so schema coverage is 100% by default. The baseline for zero parameters is 4. The description adds meaning by explaining what data is returned, but there are no parameters to clarify further.

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's purpose: to show per recommendation type how often recommendations are acted on and accuracy of predicted vs realized savings. It uses specific verbs and nouns ('get recommendation quality') and distinguishes itself from sibling tools like 'get_nable_roi' by focusing on recommendation quality metrics.

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 a 'Use when:' section with three concrete example questions (e.g., 'Which of our recommendations actually saved money?'), giving clear guidance on appropriate usage. It lacks explicit exclusions or alternative tools, but the examples effectively direct the agent.

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