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

Annotations already indicate non-read-only, non-destructive, idempotent, and open-world. The description adds significant behavioral context: specific GPU hardware (L4), a cost of $0.20 per 2 GCX, and that five models are available. These details help the agent understand resource usage and constraints beyond what annotations provide.

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?

Extremely concise: two sentences covering purpose, models, default, and cost. Every sentence adds value. However, it could be slightly more structured (e.g., bullet points for model options) to improve scanability.

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?

Covers core functionality, models, default, and cost. Missing details: output format (assumes upscaled image), input size limits, explanation of 'GCX' credits, and behavior on failure. For a tool with no output schema and 3 parameters, this is adequate but not fully comprehensive.

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 coverage is 100% with detailed parameter descriptions, especially for the 'model' enum listing all options and defaults. The description adds 'Default: 2x general upscale' and mentions content types, but this largely repeats schema info. No additional syntax or constraints beyond 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 clearly states the tool does super-resolution using Real-ESRGAN on an NVIDIA L4 GPU, lists 5 models for different content types, and specifies the default behavior (2x general upscale). This distinguishes it from sibling tools like resize_image which likely only resamples without AI enhancement.

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 explicit guidance on when to use this tool vs alternatives like resize_image or other upscaling tools. The description implies it's for quality upscaling but does not mention when not to use it, prerequisites, or alternative tools. An agent would need to infer usage from context.

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