List personalisation profiles

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the description's job is lighter. It adds valuable domain context: profiles are derived from rated images, they bias generations, and low training/ranking counts reduce effect. This helps the agent interpret and act on the returned profiles.

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?

Three sentences, each earning its place: one states the tool's purpose/output, one explains what a profile is, and one gives a practical caveat. The key purpose is front-loaded, with no redundant or filler text.

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, read-only list tool with annotations covering safety, the description is complete. It explains what the output contains (profiles plus image counts), how to use the output (pass ID to imagine), and why the counts matter. No essential information is missing.

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 is fully descriptive (empty properties), so the baseline is 4. The description reinforces the downstream usage of profile IDs but does not need to explain any input parameters.

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 ('List'), names the exact resource ('personalisation profiles'), and states the key output detail ('with how many images each was trained on'). This clearly differentiates it from sibling list tools like list_folders, list_moodboards, and list_model_ratings.

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 practical context: it explains that profiles are built from rated images and tells the agent to pass a profile ID to imagine as `profile: "<id>"`. This implies when to use the tool (before generating with a profile) and why it matters. It does not explicitly mention alternatives or exclusions, but the unique purpose makes them unnecessary.

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