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

Annotations already declare readOnlyHint=false, idempotentHint=true, and destructiveHint=false. The description adds valuable context: it runs on an NVIDIA L4 GPU, costs $0.20 per 2 GCX, and offers model variety. This goes beyond the annotations, though it doesn't disclose output format or potential side effects beyond cost.

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, dense sentence that conveys the core functionality, hardware, model options, default behavior, and pricing. No filler words; every piece of information is useful.

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?

With a fully documented schema and annotations, the description provides pricing, default behavior, and model selection context. The main gap is the absence of output format or how the upscaled image is returned, but the description is otherwise complete for an agent to invoke the 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?

Schema description coverage is 100% and each parameter (image, model, scale) is thoroughly described with defaults and enum options. The description merely restates the default (2x general upscale) and model count, adding no new meaning beyond the schema.

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 explicitly states 'Super-resolution using Real-ESRGAN' with the resource (image), mentions 5 models for different content types, and the default 2x scale. This clearly differentiates it from sibling tools like resize_image or vectorize_image.

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?

Usage is implied through 'Super-resolution' and model selection, but there is no explicit guidance on when to use this tool versus alternatives (e.g., resize_image for simple dimension changes) or any exclusion criteria. The description does not name sibling tools or provide when-not-to-use conditions.

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