upscale_image

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

With no annotations provided, the description carries the full burden of disclosing behavioral traits. It only states that an AI upscaling model is used, but omits details such as whether the operation is asynchronous, whether it modifies the input or creates a new output, whether it requires significant computational resources, or how the result is returned. The lack of such transparency is a significant gap for a tool that likely performs a heavy image-processing operation.

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 concise sentence, front-loaded with the action verb. It has no redundant fluff, and it is easy to read. However, it is arguably too sparse, bordering on under-specification, but it remains appropriate for a simple tool and does not waste words.

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 is minimal but the schema provides good parameter context and the output schema exists. The tool has only two parameters, so complexity is low. However, the description does not explain the tool's role relative to sibling tools, nor does it provide any behavioral context beyond the one-line purpose. Given the presence of an output schema, it is not necessary to describe return values, but the missing usage guidance and transparency lower the completeness score. It is minimally viable.

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 covers both parameters exhaustively (100% coverage), including descriptions for input_image (upload first if needed) and upscale_model (auto-selects first available, use list_models). The tool description itself adds no parameter-level meaning beyond what the schema already provides. Baseline 3 applies because the schema does the heavy lifting.

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's function: 'Upscale an image using an AI upscaling model.' It uses a specific verb ('upscale') and resource ('an image'), which is sufficient to understand its purpose. However, it does not explicitly differentiate from sibling tools like image_to_image, which could also process an existing image, but the term 'upscale' is reasonably unambiguous.

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 no guidance on when to use this tool versus alternatives. It does not mention scenarios, prerequisites, or exclusions. While the parameter descriptions hint at using upload_image first and listing models, these are schema-level details, not tool-level usage guidance. The description itself is a single declarative sentence with no usage context.

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