List Face Restorers

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

With no annotations, the description carries the burden of disclosing behavior. It explicitly labels the operation as a GET and a listing, indicating a read-only, non-destructive call. It does not describe response format details, but for a simple list endpoint this is adequate.

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 containing the action, resource, and endpoint. Every word earns its place, with no unnecessary repetition or padding.

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 endpoint with no output schema, the one-sentence description is complete. It names the exact resource and HTTP method, which is all an agent needs to invoke this tool correctly.

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 input schema is empty, so there are no parameters to document. The description's reference to face restoration models is enough context for the zero-parameter call, satisfying the baseline for 0-param tools.

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 a specific verb ('List') and resource ('available face restoration models') and includes the exact endpoint. This distinguishes it from sibling list tools such as list_upscalers and list_samplers by naming the precise resource.

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 makes it clear that this tool is for retrieving available face restoration models, which is a clear usage context. It does not name alternatives or explicitly say when not to use it, but for a simple zero-parameter list tool this is sufficient.

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