aiSuperResolution

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states '图片处理-超分辨率', implying a processing operation that likely modifies or enhances images, but doesn't specify if it's read-only, destructive, requires authentication, has rate limits, or what the output entails (e.g., returns a processed image URL). This is a significant gap for a tool with no annotation coverage.

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 very concise with just two words ('图片处理-超分辨率'), which is efficient and front-loaded. However, it's arguably too brief, bordering on under-specified, as it lacks necessary details for clarity. Every word earns its place, but more context would improve usefulness without sacrificing conciseness.

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 tool's complexity (image processing with potential side effects), no annotations, no output schema, and minimal description, the description is incomplete. It doesn't explain what the tool returns, how it behaves, or when to use it, making it inadequate for an AI agent to invoke correctly. More information is needed to compensate for the lack of structured data.

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 1 parameter with 100% description coverage ('图片在存储桶里的路径' meaning 'path of the image in the storage bucket'), so the schema fully documents the parameter. The description adds no additional meaning beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate.

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 '图片处理-超分辨率' translates to 'image processing - super resolution', which indicates the general purpose of enhancing image resolution. However, it's vague about the specific action (e.g., 'apply', 'generate', 'enhance') and doesn't differentiate from sibling tools like 'assessQuality' or 'waterMarkFont', which are also image-related. It avoids tautology but lacks specificity.

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?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, context (e.g., for low-resolution images), or exclusions (e.g., not for text processing). With sibling tools like 'assessQuality' and 'imageInfo', there's no indication of how this tool fits into the workflow, leaving usage unclear.

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