upscale_image

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

With no annotations, the description carries full burden. It discloses the process steps (fetch, upload, run node, return URL) and the model directory requirement. However, it omits potential behavioral details such as file size limits, processing time, error behavior, or persistence of the output URL.

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 three sentences, front-loaded with the core purpose, and contains no fluff. Every sentence adds value (purpose, process, requirement). Structure is clear and efficient.

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?

Given the simplicity (2 params, no output schema, no annotations), the description covers the key aspects: action, process, and requirement. It could mention output URL expiration or error handling, but it is mostly complete for the tool's complexity.

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 has 100% coverage with detailed descriptions for both parameters. The description adds overall process context but no new parameter-specific information beyond what the schema already provides. Baseline is 3 due to high schema coverage.

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 verb 'upscale' and resource 'image', details the process steps (fetch, upload, run node, return URL), and mentions the model types (ESRGAN, SwinIR). It distinguishes from sibling tools like refine_image by specifying the upscale model framework.

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 usage for upscaling images but does not explicitly state when to use this tool vs alternatives like refine_image or generate_image. It provides a prerequisite (requires at least one upscaler model) but lacks clear 'when-to-use' or 'when-not-to-use' guidance.

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