get_recommendation_learning

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

The description discloses that the tool is 'propose-only' and changes only what the user sees and the order, never the cloud. It explains the adaptive nature and the role of act-rate and confidence state. While no annotations are provided, the description carries the burden well, though it could mention data freshness or idempotency.

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 well-structured: a clear opening sentence, a detailed paragraph explaining the adaptive moat, and a bulleted list of use cases. It is front-loaded with the core purpose and every sentence adds value.

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 input parameters and no output schema, the description provides sufficient context about the tool's purpose, the data it exposes (act-rate, accuracy, confidence, verdict), and its role in the adaptive recommendation system. It is complete for the tool's complexity.

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 tool has zero parameters, and the schema coverage is 100% (no params). Per guideline, baseline for 0 params is 4. The description does not need to explain parameters beyond what the schema provides.

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 that the tool reveals how nable adapts recommendations based on user behavior. It specifies the resource ('recommendation learning') and the verb ('get'), and distinguishes itself from generic recommendation tools by focusing on adaptation and per-type 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?

Explicit use cases are provided in a bulleted list, including 'Why am I seeing this recommendation?', 'What recommendation types did you stop showing me?', and 'How is nable tailoring recommendations to us?'. This gives clear guidance on when to invoke this tool versus alternatives.

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