learn_preferences

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

With no annotations, the description carries the full burden. It discloses that the learned model is explainable and one-click forgettable, which signals reversibility, but it does not state whether this operation overwrites an existing model, requires at least one imported set, or has any other side effects. Partial transparency, not complete.

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 a single sentence that front-loads the action ('Learn a preference model'), then lists the specific model dimensions, and closes with two useful behavioral traits. Every clause earns its place; no filler.

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 zero-parameter tool with no output schema and no annotations, the description is moderately complete: it states the action, the source data, and a key property (forgettability). However, it omits prerequisites (e.g., must have imported sets first), what happens when a model already exists, and what the tool returns or indicates upon completion.

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?

There are zero parameters, so the baseline is 4. The description adds meaningful context by identifying that the tool operates on previously imported sets, which is the implicit input. No schema details are needed.

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 uses a specific verb ('Learn') with a clear resource ('preference model') and scope ('from imported sets'). It lists concrete model components (genre weights, ΔBPM, Camelot moves, energy steps, set shape), making the action unambiguous and distinguishing it from siblings like forget_preferences or preference_summary.

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 phrase 'from imported sets' implies the tool should be used after imports, but the description never explicitly states when to use it, when not to, or how it relates to alternatives like import_history, preference_summary, or forget_preferences. Usage is inferable but not articulated.

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