List Embeddings

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

With no annotations provided, the description carries the transparency burden. It discloses the HTTP method (GET), the endpoint, and the specific data scope (loaded/skipped embeddings for the current model). It does not discuss return format, but for a simple zero-parameter list operation this is acceptable context.

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, front-loaded sentence that states the purpose and endpoint without any wasted words. It earns its place by adding scope information not present in the title.

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 low-complexity tool with no parameters and no output schema, the description is nearly complete. It could briefly describe what 'skipped' means or mention the return shape, but the provided information is sufficient for typical use.

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, so the schema has nothing to explain. Per the rubric, a baseline of 4 is appropriate; the description effectively confirms the tool requires no arguments by omitting any parameter discussion.

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 identifies the action ('List'), the resource ('textual-inversion embeddings'), and the scope ('loaded/skipped for the current model'). It includes the endpoint, making it distinct from other listing tools among siblings.

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 implies when to use the tool: when you need embeddings for the current model. However, it does not explicitly discuss alternatives or exclusions, such as when to use list_loras or list_extra_networks instead. The scope clarification provides mild context but no direct guidance.

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