get_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 login is required, explains the merge behavior into recommend, and clarifies the layered return (user, project, effective). This provides meaningful behavioral context, though it does not describe error handling or performance characteristics.

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 concise, front-loaded with the core purpose, and every sentence adds value: it explains the standing layer, the return layers, and the login requirement. Nothing is wasted.

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?

The description covers the main aspects of the tool: what it returns, the optional parameter, and the auth requirement. No output schema exists, so the lack of return structure details is a minor gap, but the description is adequate for a simple getter.

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 schema has 0% description coverage, but the description explains the project_id parameter by stating that the project layer is returned when project_id is given. This adds meaning beyond the bare schema field.

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 tool shows the caller's standing facet layer, a specific resource, and uses the verb 'Show'. It distinguishes from siblings like set_preferences (write) and facets_catalog (list available facets) by explaining the merge behavior into recommend.

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 clear context for when to use this tool: to view the standing facet layer. It explains the different returned layers and the optional project_id. However, it does not explicitly mention alternatives or when not to use it, so it lacks exclusions.

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